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Tag: encryption

  • How Encryption Innovation Will Power Cloud Privacy for Developers in 2024

    How Encryption Innovation Will Power Cloud Privacy for Developers in 2024

    For developers, the surge in cloud computing adoption has brought both new opportunities and challenges. On the one hand, developers are turning to cloud services for data storage, processing and accessibility due to their flexibility, convenience and cost-effectiveness. And because the cloud takes care of these infrastructure and operational concerns, it’s allowing developers to focus on coding, innovation and delivering value to end-users. It also allows companies to scale more easily when their market grows or when new hardware becomes available.

    However, this paradigm shift has led to the accumulation of vast volumes of sensitive data on remote cloud servers—including personal, health, financial and proprietary business information. As the volume of sensitive data on the cloud continues to grow, it has naturally raised mounting security concerns, from data breaches and cyberattacks, to unauthorised access and the complexities in complying with specific industry regulations, such as GDPR or CCPA.

    For some, having to rely on the cloud provider to maintain robust security practices and data protection has also led to hesitancy in sharing data, both within and between organizations. This not only limits the potential for data-driven collaboration, research, and innovation—essentially halting progress in fields like health care, science, and social sciences—but businesses of all types and sizes could miss opportunities for cost savings, scalability, and efficiency if they’re not willing to adopt innovative technologies. There are also cases when companies are forbidden to do something by law because of privacy, again causing roadblocks.

    All of this has meant that up until now, maintaining data privacy while making the most of cloud computing has been somewhat of a balancing act for developers and companies. Companies want to fulfill their clients’ needs, while developers have to find technical solutions that provide the level of privacy required.

    Traditional Encryption methods Vs. Fully Homomorphic Encryption (FHE)

    Currently, there are several technologies and practices employed to protect data in the cloud, one of the most commonly used being traditional data encryption, which provides protection only during data transmission and storage.

    However, when data is processed, it must be decrypted—at which point it can expose data to eavesdropping, rendering it susceptible to potential threats. The aim of FHE is to ensure that the computations can be carried out on cloud-based machines while the data itself remains encrypted. With FHE, the data is really encrypted end-to-end: not only is it encrypted during transport and storage, but also it is encrypted during computations. It is only when the data or results are sent back to the client side that they are decrypted.

    This means that in the event of a data leak, the exposed information would be rendered meaningless without the decryption key, which stays securely in the hands of the owner. In other words, neither the cloud provider nor any potential spyware on the cloud machine would have the capability to decrypt the encrypted data.

    This approach, which ensures sensitive data remains encrypted throughout its entire life cycle on the cloud, even during processing and analysis, significantly enhances data security and privacy.

    Current Limitations and FHE’s Future Developments

    While developers using traditional cloud services previously had to decide whether to use the cloud with some privacy concerns or forgo its benefits altogether, with FHE we are starting to see a possible shift in attention from users’ reliance on the cloud provider to a more user-controlled approach.

    As FHE becomes more widespread, users gain the ability to make their computations over any cloud provider, even those they may not fully trust. This means the upfront costs of building new cloud platforms can be lowered by providing only FHE computation.

    While the encryption of data ensures that computations are secure and no unauthorized access or breaches can occur, this new tech is not without its limitations. For the enhanced security FHE offers, there may come a trade-off of longer execution times. Presently, we are mainly reliant on CPUs for computations. However it’s worth noting that as the technology advances and hardware accelerators become available, the execution time is expected to improve in the future. In fact, we anticipate that by 2026, FHE implementations will see a considerable increase in speed, potentially by factors of 100x or 1,000x, expanding the applicability of FHE to a wider range of use cases.

    Other benefits currently being worked on include usability. FHE had a reputation of being very complex and so used to be reserved for those with cryptography PhDs. By developing open source tools and extending the community’s knowledge around FHE, the aim is to democratize FHE so that anyone can use it, companies can experiment free of charge and check if it fulfills their needs.

    Looking to the future, privacy will likely remain high on the agenda for almost everyone. Thankfully, with new encryption technologies rapidly developing in terms of speed and usability, developers look set to achieve this, ensuring their cloud-based applications and services are as secure as possible.

  • Implement Object-Level Encryption and Policy With OpenTDF

    Implement Object-Level Encryption and Policy With OpenTDF

    With an exponential increase in data being generated, stored and shared online, the issue of data security no longer belongs to IT alone. Software and application developers have been put on notice along with business owners and legal teams worldwide to not only maintain data privacy but to build more secure products. There’s no easy solution. However, there are resources readily available for devs to build security into the fabric of their products. One is called OpenTDF, an open source project that lets you integrate encryption and data policy controls into your new and existing apps to safeguard your data, and the sharing of it, for you as well as your users.

    What’s TDF, and Why Should I Use OpenTDF?

    TDF, the “trusted data format,” was originally developed by the United States National Security Agency (NSA). It’s an open standard for object-level encryption that keeps data protected and under the data owner’s control, wherever it’s created or shared. TDF includes cryptographically secured metadata that ensures consistent policy control throughout the data life cycle. Picture this: You can grant, revoke or “turn off” data access at any time, even if the data has left your network or application.

    OpenTDF is an open source project that evolves the open TDF specification and provides a blueprint for getting started. There are a multitude of example applications that demonstrate the implementation logic, as well as streaming video and IoT use cases.

    The OpenTDF project is based on Kubernetes and OCI containers, and there is a quick start guide to get you up and running with a development environment. The quick start process will install supplemental services like Keycloak as well as project-specific services like key access service (KAS) and Abacus (an ABAC front end for configuration and management of attribute-based access control). Once you’ve completed a quick start installation, you’ll have a basic OpenTDF cluster with a Keycloak identity provider, PostgreSQL data store and a single entry point at localhost with an Nginx ingress controller.

    There’s an architectural diagram available on GitHub to see all of the services and service interactions.

    Several SDKs are available for building on the OpenTDF framework, including JavaScript, Python, C++ and Java. The client SDKs generally include basic examples for identity auth and creating a TDF-protected encrypted object.

    Take OpenTDF for a Spin: Secure Remote Storage

    Have you ever wondered how to make data access secure and simple? Using OpenTDF, developers can create that experience for their users. Let’s walk through a sample web application that uses OpenTDF to encrypt and upload data to cloud storage.

    The application we’ll be using is called OpenTDF Secure Remote Storage. It’s a react-based example that shows developers how to create encrypted data streams. These streams allow you to upload and download files from S3-compatible remote data stores while maintaining data protection. You can even remove encryption if needed.

    To make things easy, we’ll be using OpenTDF’s client-web SDK, which authenticates against Keycloak using OpenID Connect (OIDC). Keep in mind that this example runs on your local machine. It’s not designed for cloud or enterprise services.

    Prerequisites: You’ll need an S3-compatible storage object, like an Amazon S3 bucket. (You can create one for free here.)

    Set it Up

    First, install two CLI tools: Kind and Tilt. These will be used to deploy the OpenTDF services to your local machine. If you’re on macOS, you can install them with a simple Homebrew command: brew install kind tilt.

    Next, you will need the sample code on your local machine. You can either download the zip or clone the OpenTDF GitHub Repository using the following command: git clone git@github.com:opentdf/opentdf.git. This will create a directory called “opentdf” in your current location.

    Now, navigate to the root directory of the sample application: cd opentdf/examples/secure-remote-storage. To deploy OpenTDF, you’ll need a local Kubernetes cluster. Use the Kind CLI to create one: kind create cluster --name opentdf.

    Finally, start the application using Tilt: tilt up. This will launch the necessary OpenTDF services.

    Steps to Run the Application

    1. To begin, go to http://localhost:65432/secure-remote-storage in your web browser. This is where the Secure Remote Storage webpage is hosted.

    2. Now, it’s time to log in. Use the following credentials (defined in the bootstrap’s config file):
    ○ Username: user1
    ○ Password: testuser123

    3. Then, choose a file from your computer to upload. It can be anything – a text file, a PDF, or an image. Let your creativity flow! If you don’t have a file handy, no worries. You can download an image by right-clicking on it and selecting “Save As” from this link.

    4. Next, tell the application where to store your encrypted file. Provide the necessary JSON object that defines your S3-compatible object store. You can refer to the prerequisites for more details. (Optionally, you can save the configuration for future uploads. Just give it a name and click “Save.” This way, you won’t need to define the object store again in the future.)

    5. Now, it’s time to encrypt and upload. Click the “encrypt and upload” button, and watch the magic happen!

    What’s happening behind the scenes? When you click “encrypt and upload,” the application uses the OpenTDF API to convert your selected file into a .tdf file. It applies AES-GCM encryption and attaches access controls to ensure that only authorized users, like you (in this case, testuser123), can access the data. Even if your data store is public, your data remains secure.

    6. Ready to view and download your uploaded file? The table on the webpage lists all the files you’ve successfully uploaded. Each file has a download button next to it. Click that button, and the hosted file will be downloaded and decrypted on your local file system.

    Excited to explore more? Now that you’ve seen OpenTDF in action, you can dive into the source code of this application. Use this sample application as a starting point to integrate OpenTDF into your own secure applications.

    What Else is Possible With OpenTDF?

    From a secure webcam app to a privacy-forward menstrual tracking app and more, the possibilities are endless with OpenTDF and the future is in developers’ hands. By building on OpenTDF, the valuable data flowing through your applications will be protected forever.

    Learn more about OpenTDF at openTDF.io, and get the full quick-start guide, including more detailed instructions and other sample apps at the OpenTDF GitHub.

    Cassandra Zimmerman, technical product manager at Virtru, contributed to this article.

  • Developers’ Role in Protecting Privacy

    Developers’ Role in Protecting Privacy

    We live in a world where we always expect things to happen seamlessly. Every time somebody walks into a café and signs in to the free WiFi and opens their Instagram or TikTok to stay up-to-date with trends and friends or retrieves a discount from a retailer by joining their newsletter circulation, rarely do they stop to think about how that convenience and value is derived.

    Technology is the key enabler working away in the background, and developers and DevOps teams are making that happen. Underneath the slick interfaces of the apps and websites that provide these highly efficient and personalized experiences is a complex machine that relies on data to fuel it, built by developers and delivered by DevOps teams.

    In many ways, data is a two-way exchange, and those end-users that are aware of this give and take appear to be happy with how the relationship works.

    Indeed, several surveys in recent years suggested that the majority of consumers are happy to share their data with companies if it improves their experience. Furthermore, a significant minority are still comfortable if they have no control over who sees their data, there is no clarity around how data is stored and used, even if the organization in question has a poor reputation for data privacy.

    The Privacy Balancing Act

    So, if most people already accept data trade-offs in some shape or form, should developers still care about building applications and solutions that guarantee data privacy?

    The short answer is yes, but as developers, that involves a tricky balancing act.

    Although sharing data has become commonplace in exchange for benefits and value, consumers are becoming more aware of privacy issues. Take the EU’s General Data Protection Regulations (GDPR) as an example. Over the past five years, awareness has more than doubled in notable European markets such as the UK, Spain, Germany, the Netherlands and France.

    Meanwhile, there is also commercial pressure, as employers rely on developers to innovate to remain profitable. At the same time, customers expect brands to be responsible with their data, and failure to do so at the expense of trying to commercialize a new application could be detrimental. Indeed, while the pandemic may have ushered in significant changes and altered consumers’ attitudes toward data privacy, end users remain unwavering about the importance of security.

    Maintaining this balancing act is becoming increasingly complex to achieve. However, the question of data privacy is becoming a key business priority, and that means developers have a big opportunity to show their commercial value to their organizations. Indeed, companies cannot afford to get the balance wrong, and developers are central to creating the perfect formula.

    In fact, the privacy conundrum has been around for a long time.

    When the internet was created, privacy was not considered an issue because all content accessed online was deemed to be public. Nothing was encrypted; the introduction of email complicated things, not least because anybody on the internet could, theoretically, intercept personal data and read private messages.

    This prompted the emergence of the cypherpunks, a group of cryptographers who began to contemplate what the future of digital privacy might and should look like. They started to create the tools they believed the world needed to safeguard data privacy–today, their mission is carried on by developers who value user privacy.

    Why Developers Need to Be Familiar With FHE

    However, securing the transmission of data is not enough, as many breaches still occur because data isn’t encrypted during processing.

    So, in a world where more and more data needs to be shared to deliver the sort of experiences consumers expect, is it inevitable that privacy and security will be compromised as the volume of data exchanges grows? Not necessarily.

    Fully homomorphic encryption (FHE) offers a solution, allowing developers to build applications and run services without needing to see or secure the underlying data. This is because FHE enables data to be processed blindly without having to decrypt it at any stage. FHE offers a way for developers to maintain that all-important balancing act between maintaining privacy and offering value to both business and customer.

    In FHE, to guarantee a user’s data privacy, all data is encrypted by the user with their own secret key. Users of a particular service, such as an online medical portal, will use a secret key to send their encrypted data to a server where blind processing takes place. The result is also encrypted and sent back, the user again using their secret key to decrypt the information.

    What this means is that, because data is encrypted end-to-end, the company providing the service can do so in a way that keeps the same customer experience, even though they cannot see the data that is being shared and sent back. From a security perspective, this is also critical. Governments, attackers and service providers cannot see the exchanged information because they do not possess the key–they also cannot break it, as the encryption used in FHE can resist attempts made even by powerful quantum computers.

    Privacy can also be protected during larger-scale data exchanges that underpin collaboration between organizations and nation-states on numerous strategic programs, with each contributor able to add their value knowing that their intellectual property and sensitive information is not compromised.

    Looking ahead, if developers can embed FHE into their practices on a broad scale, then a key milestone for privacy is within reach. Rather than being viewed as an objective relating to digital interactions, privacy can be treated as a naturally occurring by-product of every application, piece of software and service that the developer community creates.

  • FAA Ground Stop due to Technical Debt? | Don’t Do DIY Crypto!

    FAA Ground Stop due to Technical Debt? | Don’t Do DIY Crypto!

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: The FAA’s NOTAM database gets corrupted, and Threema shows why DIY cryptography is bad. (more…)

  • IBM z16 Delivers AI and Quantum-Safe Levels of Security

    IBM z16 Delivers AI and Quantum-Safe Levels of Security

    Business transformation and disruption is all about data. But it’s not just about collecting huge amounts of data, it’s what you do with that data and how it can enable you to create powerful insights.

    While many people focus on the cloud unicorns, the reality is that industry incumbents have a huge advantage. These businesses have decades of experience which translates into massive amounts of data and business logic. Where do many of these businesses host their most important workloads and data? IBM zSystems—the system that is best known as “the mainframe.” 

    Today, IBM is announcing its latest IBM zSystem—IBM z16. Two weeks ago, IBM spent time briefing the industry analyst community on the business strategy and technical underpinnings of the IBM z16. We met with a variety of IBM business leaders including executives from the IBM Systems division, Distinguished Engineers and IBM cloud leaders. The most significant updates to the platform are the inclusion of a dedicated on-chip AI accelerator and the addition of quantum-safe cryptography.

    It’s important to understand the size of the IBM zSystems ecosystem. The platform is used by 45 of the 50 largest banks in the world, four of the top five airlines and eight of the top 10 insurers, among others. Likewise, the partner ecosystem is huge, ranging from the largest system integrators to small DevOps- and AI-focused technology vendors. Suffice it to say, some of the world’s largest businesses and governments run on IBM zSystems.

    In this article, I will briefly cover the five biggest takeaways from the pre-announcement industry analyst briefings.

    IBM is Bringing AI to the Mainframe

    Many cloud data warehouse and analytics vendors say “Bring me the data.” IBM is flipping the script, because IBM zSystems already houses clients’ most critical customer and operational data. IBM z16 will be shipped with an on-chip integrated accelerator for AI.

    Everyone knows that AI creates a competitive advantage, so why should you care about the on-chip AI accelerator? The last mile of deploying machine learning/artificial intelligence (ML/AI) models is still a huge challenge. Businesses are investing large amounts of money and skills in developing models; however, latency often kills. The difficulty is that ML/AI models need to be built into applications so that the model can be executed at the point of transaction with virtually no delay. This also must be done at a massive scale! Think about the latency and security concerns if near-real-time banking transactions needed to go from a back-end system to a cloud AI platform to check for fraudulent transactions.

    For those of you who have been following the IBM zSystems platform, IBM’s z14 unveiled a pervasive encryption capability. This encrypt-by-default approach without traditional encryption overhead was enabled through an encryption-dedicated chip. Similarly, AI workloads will be performed by the integrated AI accelerator.

    Your AI Infrastructure is Only as Powerful as Your Software Ecosystem

    Developers and data scientists want to use data, ML and AI tools that they know and trust. AI success requires an open stack of software options, not a walled garden. While IBM has a wide range of AI technologies, including Watson Machine Learning for z/os, IBM Cloud Pak for Data and IBM Cloud Pak for Watson AIOps, just to name a few, an IBM-only approach would be a non-starter for many data scientists.

    The IBM z16 AI accelerator supports a wide selection of ML libraries and compilers. Clients can build and train models in a variety of popular frameworks including TensorFlow, SAS, Microsoft CognitiveToolkit (Microsoft CNTK), PyTorch and Keras, along with others.

    The system uses ONNX, an open source tool to enable interoperability of ML/AI models to prepare and deploy the model on the dedicated AI accelerator.

    What does this mean? You can bring your existing data models to IBM z16. In addition, your teams can use their existing preferred vendors while also working with IBM zSystem’s integrated accelerator chip.

    Cybersecurity Arms Race: Quantum-Safe Cryptography is a Growing Need

    Cybercrime is a multibillion-dollar business. Although fairly routine exploits like phishing remain the number-one money generator for cybercriminals, these gangs and nation-state-sponsored organizations are getting more sophisticated. For example, cybercriminals are investing in AI and ML just like traditional businesses. In some cases, cybercrime syndicate engineering employees believe they’re working for a legitimate company.

    What is quantum-safe cryptography? The research is focused on identifying algorithms that are resistant to attacks by both classical and quantum computers. 

    Why is this a growing area of concern? IBM, along with Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) all offer quantum computing-as-a-service. While quantum computing has massive potential for applications across a wide range of industries including drug discovery, chemical engineering and combating climate change, keep in mind that cybercriminals are looking at this technology to unlock complex cryptography algorithms. 

    IBM claims that IBM z16 is a “quantum-safe system.” The company uses a variety of different hardware, software and business process approaches to assure data security. For example, the system includes a new crypto express card with quantum-safe APIs to upgrade existing application security. At a firmware level, IBM z16 has a built-in dual signature scheme.

    In addition, for a number of years, the IBM zSystems platform has taken a zero-trust approach to security which includes a bring-your-own-key (BYOK) framework. IBM can never access the keys to your encrypted data–and neither can cybercriminals and/or governmental organizations—instead, those encryption keys remain inside your firewall.

    IBM zSystems Tech Goes to the Cloud; Mainframe Remains On-Premises

    What a difference a nine can make! Five nines mean 99.999% availability while four nines (99.99% availability) result in more than 47 minutes of downtime per year.

    When I work with C-level executives that have a mainframe within their environment, the role of IBM zSystems is always discussed. In many cases, businesses have explored the possibility of transitioning off of the mainframe, but resilience, predictability and security are the reasons why businesses remain on the platform.

    IBM mainframe services are increasingly being offered in the cloud. Most recently, IBM announced IBM Z testing in the cloud. Likewise, IBM’s Cloud Hyper Protect Crypto Services, a key management approach using a specific security chip module, is enabled by IBM zSystems technology.

    However, you shouldn’t expect mainframe-as-a-service. Between latency, security, resilience, governance and compliance, it’s going to be a long time until you migrate and/or send your mainframe workloads to the cloud.

    As the author of Hybrid Cloud for Dummies and Cloud for Dummies 2nd Ed. (among other books and articles) I can confidently say that the cloud is not a panacea. The business case for moving many mainframe workloads to the cloud doesn’t exist. 

    Digital Transformation Means More Than Slick Apps–Plumbing Matters!

    Don’t get me wrong, mobile check deposits, airline ticketing and contactless hotel check-ins are great, but in many cases, they rely on older systems that have not been updated. 

    A few weeks ago while traveling for work, my flights were delayed for hours. I saw that another flight was leaving within the hour. I was already at the airport and figured I could rebook it via the airline’s app. Of course, it wasn’t that easy—I finally went to the ticketing desk and told the agent that I’d like to book a different flight. The agent then went to work; they spent a long time entering information into their system, talking to a colleague, asking for help and asking me several questions. After about ten minutes my flight was finally rebooked.

    This airline has a great app experience when things are running smoothly (so great, in fact, that I prefer to book my travel on the app rather than the computer). However, even a slight disruption caused everything to go off the rails. It’s clear to me that the company still relies on old, broken processes and outdated technology.

    My Perspective

    Moving AI closer to the data just makes sense. The last mile of AI—moving models into transactions—continues to be a huge challenge. I work with many businesses that have tons of data and do an excellent job retrospectively analyzing that data. However, real-time data-driven decision-making remains a challenge because of latency and the fact that moving data between on-premises and cloud systems raises security and governance risks (in addition to costs).

    Quantum-safe security is a growing concern for highly regulated organizations in the banking, insurance and governmental segments. Over the last several years, financial industry CIOs and CISOs/CSOs at large financial services organizations were focused on establishing a zero-trust approach to security. However, as attacks become more sophisticated, security teams are starting to perform risk assessments on their approach to cryptography. The inclusion of quantum-safe cryptography is important for IBM zSystems customers.

  • Lapsus$ Shames Okta/Sitel | Bitcoin Nukes Climate | EU DMA E2EE FAIL

    Lapsus$ Shames Okta/Sitel | Bitcoin Nukes Climate | EU DMA E2EE FAIL

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Okta and Sitel under fire over Lapsus$ hack, Greenpeace and others call for bitcoin change, and Europe still hates encryption. (more…)

  • How Quantum Computing can Solve Real-World Problems

    How Quantum Computing can Solve Real-World Problems

    The history of technology can seem like it was very predictable in retrospect, but the future typically feels more uncertain. Nowhere is this uncertainty more evident than in the domain of quantum computing. When the spectrum of possible outcomes spans from “quantum computers will be one of the most important technology developments of all time” to “quantum computing may never really become practical enough to justify using over a classical computer alternative,” trying to make forecasts can seem futile. However, as a technologist, investor, consumer or just a curious observer, the boundaries and texture of the uncertainty itself can be very interesting, valuable and informative. 

    Quantum computing isn’t likely a replacement for today’s classical computing—at least not initially—but rather a potential complement to it for specific applications. A 2021 report by Deloitte lists dozens of applications that could be transformed by quantum computing approaches: Protein folding, fluid simulation, credit underwriting, financial risk analysis, supply chain optimization and forecasting, vehicle routing, fraud detection, fault analysis, weather forecasting, semiconductor chip design, product-portfolio optimization, consumer product recommendations—and this is only half the list. 

    Quantum computing could make previously intractable simulation, search and optimization calculations relatively easy and quick. For some kinds of calculations, a quantum computer could be mind-bendingly faster than a powerful classical computer could ever be. While today’s classical computers running machine learning systems can make incredibly accurate predictions, quantum-based systems can, in theory, far outstrip their speed at certain prediction-related tasks.

    The blend of physics, math and computer science behind quantum computers is fascinating, but complex. Computer scientist Scott Aaronson, one of the best science communicators, wrote a recent essay for Quanta magazine called, “Why Is Quantum Computing So Hard To Explain?” 

    To oversimplify, quantum computers are built around “qubits,” which are subatomic-scale components, often kept at incredibly cold temperatures (think: Absolute zero) to minimize interference from the rest of the universe. These components can be put into a quantum physics state called “superposition” that allows them to, in a sense, take on many potential values at once. Even though their value is uncertain, reliable calculations and transformations can be performed on these qubits while in a superposition before being reduced to a more classical state, effectively parallelizing computations. Qubits in a quantum computer can also be entangled with one another (a phenomenon that Einstein referred to as “spooky action at a distance”), harnessing them as a team to increase the variety of logical calculations that it’s possible to compute. For certain types of math—for example, searching for an optimal outcome from myriad options—quantum algorithms can seem close to instantaneous when compared to those that run on classical computing hardware. 

    Quantum Computing Challenges

    Publicly available quantum computing has been on the horizon for decades, yet there’s still no reliable forecast for when products capable of widespread adoption will get released, exactly what form the systems will take or how broad and deep quantum computing’s impact will be. There are many technical challenges when it comes to getting a broadly applicable quantum computer to be useful at scale. And quantum computers are racing against classical computers, which have also been improving very rapidly in power, versatility and ease-of-use. 

    One important bottleneck for quantum computers is that correctly reading the results of a quantum calculation is prone to a very high error rate—the delicate superposition state can deteriorate before the correct result is presented as an output.

    Aaronson explains it:

    The problem, in a word, is decoherence, which means unwanted interaction between a quantum computer and its environment — nearby electric fields, warm objects, and other things that can record information about the qubits … The only known solution to this problem is quantum error correction… But researchers are only now starting to make such error correction work in the real world. When you read about the latest experiment with 50 or 60 physical qubits, it’s important to understand that the qubits aren’t error-corrected. Until they are, we don’t expect to be able to scale beyond a few hundred qubits.

    Some theorists even believe a practical quantum computer is impossible to build because of the inherently high amount of “noise in the system.” Mathematician Gil Kalai predicts that doing error correction to reduce that noise to a usable level would, by basic computing theorems, restrict the computer’s number-crunching capacity so low as to be not worth using.

    Nevertheless, researchers continue to chisel away at the challenge of error correction and to pass key technical milestones. For example, in July, Google researchers published a paper in Nature explaining how they have found a promising error-reduction method that scales as more qubits are added to a system. 

    Hardware, Software and App Landscape

    There are several different approaches to quantum hardware. Rigetti and Oxford Quantum Circuits use superconducting quantum circuits, a more widespread approach favored also by Google, Microsoft, and IBM. IonQ (which is planning to go public via a SPAC transaction) and Honeywell (which recently announced a combination of its quantum computing unit with UK-based Cambridge Quantum Computing) use trapped ions. D-Wave uses quantum annealing, ColdQuanta is using cold atoms and Xanadu is using photonics. Each approach has its pros and cons, and each hardware technology faces a fluid set of shifting bottlenecks in reaching affordable scale. Work at the materials layer continues to advance as well, for example, Raicol Crystals is growing quantum quasi-phase matching crystals that can be used in applications as varied as quantum computing, sensing, encryption and communications. And it’s not certain that there won’t be another approach that leapfrogs them in solving the challenges of error correction and price.

    Other companies, such as Quantum Machines, Q-CTRL and Seeqc, are building control hardware that can operate in the space between classical and quantum hardware. Control systems can exist at temperatures warmer than absolute zero, but colder than room temperature. Some of these systems involve both software and hardware and can perform tasks like monitoring the quantum processor’s performance, performing error correction or serving as an abstraction layer connecting classical and quantum hardware. 

    There are also companies creating quantum computing application layer software, like Zapata Computing, Riverlane or QC Ware. This can include application frameworks, components, algorithms or even turnkey quantum computing applications. Classiq has software for quantum circuit synthesis. There’s also a lot of interesting fundamental research happening at the software intersection between quantum computing and artificial intelligence – e.g. Zapata published work recently using IonQ’s ion trap quantum system to train a machine learning system to generate high-quality handwritten digits with low error rates. 

    One of the first theoretical uses for quantum computing, proposed in 1994 by mathematician Peter Shor, would be to crack the RSA encryption widely used today to protect data. The threat spawned research into post-quantum cryptography, with researchers proposing multiple new approaches to encryption that would need to replace today’s algorithms to preserve the privacy of data. Companies such as ISARA (quantum-resistant encryption) and Quantum Xchange (an out-of-band, symmetric key delivery platform) emerged to help prepare governments and companies now for a world where quantum-based, encryption-cracking algorithms start to work on breaking today’s encryption.  

    There are several widely used software development kits (SDKs) that let developers write software for a quantum computer without actually having one to run it on. In fact, a kind of format war has already broken out between quantum computing development environments.

    One widely-used quantum SDK is IBM QISKit, an open-source Python-based development kit for prototyping software and devices on simulators or IBM Quantum Experience, a cloud-based quantum service. ExxonMobil, Goldman Sachs, Boeing, JP Morgan, Samsung and PayPal are just a few of IBM Quantum’s marquee customers. Google Cirq, another Python-based development kit, touts Volkswagen as a user, and Volkswagen is also a contributor to Google’s TensorFlow Quantum, an open source library that extends Google’s TensorFlow machine learning algorithms to quantum processors for rapid prototyping. Microsoft’s Quantum Development Kit (QDK) is based on Microsoft’s Q# programming language and brings quantum compatibility to Microsoft Azure cloud services. Early customers of Azure Quantum include Dow for chemical simulations and Toyota Tsusho, Toyota’s trading arm, developing traffic light optimizations to reduce urban traffic congestion.

    In a recent piece, The Information noted that “Amazon is looking to hire more than 100 new quantum computing scientists, hardware developers and engineers within its Amazon Web Services cloud business,” following the enormous amount of funding flowing to startups in the space, such as Quantum Motion, Atom Computing and PsiQuantum. Amazon also launched Amazon Braket, a fully managed quantum computing service that gives customers access to hardware from a diverse set of vendors such as IonQ, Rigetti and D-Wave. 

    It’s also possible that the most valuable quantum platforms won’t be versatile, horizontal platforms at all; rather, the space could evolve instead to include special-purpose machines, or quantum application-specific integrated circuits (ASICs). Unlike the way things have played out with classical computing, this could mean that quantum computers would be highly specialized integrated hardware and software machines tailored to specific applications, e.g. fluid simulation or fraud detection. 

    Early use cases may involve quantum systems taking specialized roles as part of a larger classical system, similar to how NVIDIA’s graphics processing units (GPUs), which often run alongside an advanced CPU, can be used to offload specific computations for which the GPU is well-suited. In this model, where quantum components are an “accelerator” to classical systems, a credit underwriter might use a quantum processor to handle one of the tasks involved in scoring applicants and might use a different special-purpose quantum processor to perform a piece of a calculation to optimize its borrower portfolio. 

    For many use cases, the error rates of calculations are still too high for developers to do more than hone their coding chops on quantum simulation platforms, waiting until quantum computers start to really work at scale. Quantum simulators, such as QMware, make quantum software algorithms and applications believe they are running on actual quantum hardware, but they are really running on classical computers pretending to be quantum computers. 

    In the meantime, to manage some of the timing uncertainty, quantum-related startups can pursue profitable projects with government, corporate and university buyers today as a source of funding and requirements. Like some biotech startups, they can do this while assembling a strong team and seeking an early adopter clientele, progressing through scientific milestones while keeping an eye on future commercial markets.  

    The hurdles to generally available quantum computers are much like those faced by alternative energy sources: Unlocking the potential will require breakthroughs in hardware design, lower manufacturing costs and a driving force to spur large-scale adoption. And many approaches won’t work. 

    And as with any technology, it is, of course, possible that software developers will find valuable applications for quantum processors that people haven’t thought of yet today—just as developers found that GPUs meant to handle graphics processing also excel at training highly parallelized machine learning models.

    That said, a broadly useful and effective quantum computer’s arrival has the potential to be as disruptive an opportunity as the creation of the Internet or the use of machine learning to make predictions. 

  • DevOps Chats: Swiss, Simple Secure Collaboration with Adeya

    DevOps Chats: Swiss, Simple Secure Collaboration with Adeya

    The current situation with so many working from home has shined a spotlight on potential security issues with many of our collaboration solutions. Adeya, says they have the answer, offering Swiss, simple collaboration on a military grade scale.

    I sat down with Francois Rodriguez of Adeya’s executive team to discuss what makes Adeya both simple and secure, as well as what the offering is all about. This is more than video conferencing by the way. This is a well developed and tested collaboration suite that has been in use by the Swiss military and others for years. Have a listen and check out Adeya.

    As usual, the streaming audio is immediately below, followed by the transcript of our conversation.

    Transcript

    Alan Shimel: Hey, everyone, it’s Alan Shimel for devopsy.kinsta.cloud. You’re listening to another DevOps Chat. We’ve got a new company to introduce you to on today’s DevOps Chat, and the name of the company is Adeya. I hope I didn’t mispronounce that, but my guest will give us the proper pronunciation. I’d like to introduce you to Francois Rodriguez, he’s the chief growth officer at Adeya. Francois, welcome.

    Francois Rodriguez: Thank you for hosting us.

    Shimel: Thank you. So, Francois, did I get the name right?

    Rodriguez: Yeah, correct. The pronunciation is correct. Americans tend to say Adeya, but yes, Adeya is correct. [Laughter]

    Shimel: Okay. Well, you know, I always thought I had a little bit of a French accent. [Laughter] But I’m glad I got it right, then. So, let’s start with the obvious, Francois. Our audience may not be familiar with Adeya. Why don’t you, if you don’t mind, give us a little background?

    Rodriguez: Yeah, the background of the company is the following. So, it is a spinoff from EPFL. EPFL is one of the top five universities in technology in the world, based in Lausanne, so next door. So, for those that don’t know where Lausanne is, it’s next door to Geneva. And from that spinoff, the ask was for encrypting voice communication for the Swiss Army at that time, using a Symbian Operating System. At that time, Nokia was the leading mobile phone operating system, and that was the foundation of the company.

    With the advent of the smartphones, the application has been enriched with messaging and then the mobile application becomes, as well, a desktop application, a tablet application, enabling as well file sharing, instant messaging and we’re introducing as well video conference calling, end to end encrypted. So, that’s the most important element of the suite is the encryption layer, initially deployed and developed for the Swiss Army, so we are using the highest grade of encryption, so called military grade. And we are bringing that technology to companies and small organizations as well.

    Shimel: Excellent. So, the Swiss Army is already a customer, is that correct?

    Rodriguez: It’s the historical customer.

    Shimel: Okay. And then, at this point, has the company raised venture capital? Is it self-funded? What is the situation?

    Rodriguez: Yeah, it’s a venture capital, which is important in this type of business. You certainly have heard the news of cryptogate that was having some links with governments. So, these days, it’s very important when you provide this type of technology that you don’t have strings with any organization state owned or having any back door.

    Shimel: Sure. Sure, I mean, everyone’s worried about that. And, you know, as—well, before we jump more into that, Francois, what about your personal background, your journey? How did you come to be chief revenue officer—excuse me, chief growth officer at Adeya?

    Rodriguez: So, I started my career in telecom. At that time, I was in charge of developing the multimedia content for the youth and student populations. Before the advent of the smartphones, multimedia was games, ring tones. I don’t know if you remember these days.

    Shimel: Sure, I do.

    Rodriguez: And then we developed the music content, the early days of .mp3s. And moving forward, I professionalized my expertise in multimedia, transforming application into more professional applications. With the arrival of the tablets, we develop more and more professional application for enterprise, and then I joined a brand new spinoff from Airbus Enceda to bring connectivity on airplanes. The company used to be called OnAir.

    Shimel: Mm-hmm.

    Rodriguez: You’ve got Gogo in the U.S., so it’s a similar company, providing connectivity to passengers, multimedia and content to passengers, but as well, professional application for the cabin crew, electronic flag backs for the pilots, and more recently, IUTs connecting landing gear, engines, and any data that the airplane was generating.

    Shimel: Sure. Fantastic.

    Rodriguez: I joined Adeya in 2016.

    Shimel: Also, how long has Adeya been around for? I didn’t realize. When was Adeya founded?

    Rodriguez: 2007.

    Shimel: Oh, really? So, it’s been—so, this is not some fly by night, started yesterday company. You guys have been at this a while.

    Rodriguez: Yeah, exactly. Yeah, the brand awareness is really recent, because the typical client of Adeya used to be governments that are quite shy to do any marketing around, and it’s really when I joined the company that we started the blocking and enterprise product.

    Shimel: You know, in some ways this reminds me of—so, I’m a little older than you. [Laughter] But, you know, back when we had the AOL message rooms or chatrooms, whatever—they called them meeting rooms, maybe, before then. It was a rather innocent time on the internet where people didn’t realize you can’t let your children go online and just go into some chatroom with people you don’t know and what can go on there. And of course, we saw horror stories of what people were writing in chatrooms being made public or used the wrong way or predators, you know, preying on women and children and stuff. And because—well, it’s the reason why we can’t have nice things, right? There’s always bad people trying to take advantage of things.

    And then, as a result of that, you know, we saw a locking down, if you will, somewhat, of a lot of that kind of technology. But then, I guess it’s just human nature, people want to communicate, right? People want to communicate—even, God knows now, when we’re all locked in our houses, right, people want to still communicate. Being alone and isolated is just kinda foreign to the human condition.

    Rodriguez: Yeah, you’re right. So, I used to put an application on my daughter’s phone to lock the phone after an hour. These days, we’re up to three hours, because, you know, they need to communicate. They don’t go to school any more, so they need to be connected to friends, they need to chat, they need to send pictures, to send videos, to do video calling. These days, video calling is very important for the youngsters. I’m usually not a very big fan of video calling, but they are, so those are—

    Shimel: No, they’re native. They’re native to this, right? To us, we look at it and—actually, I was having this conversation with my COO this morning, talking about, we both  have children who are in high school, college now and in university.

    Rodriguez: Yes.

    Shimel: And, you know, they’re net native people who, the idea of doing video calling, the idea of listening to podcasts and so forth, this is how they’re used to consuming, right? And [Cross talk].

    Rodriguez: They’re used to the technology, they’re used to the technology and they’re also very tech savvy in terms of what features and functions those tools can bring, and they are very, very, I would say, picky on which tools they’re gonna use. And we’ve seen that in certain companies that there is a kind of step back when they propose traditional tools that are quite clunky, quite difficult to use, very slow that you need to put the VPN around and all these old fashioned or traditional tools have been rejected by the young population of workers.

    Shimel: Yeah.

    Rodriguez: And more so now with these days of remote workers where you have, you know, a layer of Bring Your Own Device on top of it where people are installing their own favorite applications, too. It’s very difficult to control. It’s very difficult to put security around when you don’t have a clear policy on what is allowed, what is not allowed, and more so when you don’t bring options. People are starting to download their favorite tools online. Obviously, security is not their top priority, it’s to make their work easy and fast and provide agility to the organization. So, that’s a real issue.

    Shimel: I agree. And you know, I mean, but this is a classic security conundrum, which is, if we just think we’re gonna lay down on the tracks and say, “Stop!” the train runs us over, right? We can’t stop that, so what we need to do is, instead of being the people who say no, we need to be the people to say, “Yes, you can, but here’s a secure way of doing it that maybe isn’t too cumbersome, too expensive, and still allows them to do what they need and want to do from a communications perspective.”

    And I guess, really, that’s what the whole Adeya thing is about, right, is—hey, you wanna video or do voice calls or, you know, messaging, you can still do that, and we understand it’s important. But here’s a way of doing it in a more secure fashion.

    Rodriguez: Yes, security is important, but what we’ve been putting at the heart of the development is the user experience. Because there is a lot of tools in the security space in the market, but very few of them are really providing a seamless experience for the users, and more so when they’re not IT people. Because IT people will always tell you, “That’s very easy, you go in Settings, you configure that, and you put the VPN around and it’s working,” when in fact, nobody knows how to do it.

    So, what we’ve been doing is to bring in one unique application, I’m calling the Adeya like a Swiss Army knife. In one tool, you have got different features. You don’t need to have, you know, upswinging. You don’t need to have configuration. The security layers are ___, you instill it in your device, and you start triggering the feature that you want to use.

    Shimel: Absolutely. Francois, if you could share with our audience, you know, how easy and how much does it cost if you wanted to use the Adeya solution? Let’s say—look, I’m a little worried about our use of Slack and/or Zoom for corporate communications; you wanna use what you wanna use with your kids or whatever, that’s one thing. What does it take to stand Adeya up?

    Rodriguez: So, we’ve got four packages, depending on the security layer that you want to activate. So, the starting price point is $4, the $4 is already, you know, the starter package that provides you one to one video calls, security voice over IP, instant messaging, and as well, you know, all the Dropbox and Lockbox where you can store some documents.

    If you want to have the video conferencing with multiple users, you have an extra option, and if you want to install everything on premise on your own data center, and this is what we do for governments, there is an extra cost to deploy the system, but it’s your entire system, okay? You have no backdoors, no cloud act to open up your data center to foreigners, and you have everything in your house. So, that’s the model that we do.

    So, we’re starting with a very easy to set up cloud solution based in Switzerland and we use the strength of the cloud infrastructure in Switzerland with the data sovereignty, and we go up to deploy your own system on prem.

    Shimel: Really, okay. So, there’s sort of a cloud-based system, if you will, SaaS or cloud-based, and then you have a choice of on-prem. Because I know in many government situations, as you mentioned, they don’t want to share public infrastructure, right?

    Rodriguez: Yeah.

    Shimel: And so, that’s a real requirement. So, Francois, I’ve got to ask you, I mean, it seems like the Adeya system would find a very fertile ground in today’s environment with what’s going on with COVID, with people working from home and everything else—how has this affected, or how has Adeya responding? What are you seeing in the market recently now, just in the last couple weeks, as a result of the pandemic?

    Rodriguez: So, what we see is a variety of demands coming from the historical clients which are government administrations that are really expanding the fleet. It’s not, you know, just to secure a certain special project, but it’s really to provide everyone the tool to work from home, and in the most important security layers. Because, as soon as you leave the government institutions, you need to be connected from whom, with high level security, because then you bring the formation outside the typical firewalls, and this is what the governments are fearing.

    Now, the diversity that we’re seeing is that we start having—hello?

    Shimel: No, no, it’s okay. People are blogging, I have a meeting coming on.

    Rodriguez: Okay, understandable. What we see is, enterprises now coming to equipping their entire organization and not just the top executives that used to travel a lot or go to countries where they need to shield their communication, but as well to protect their messaging, to protect their messaging, to protect the documents that they’re sending, avoiding sending them over e-mail because even though e-mail has been encrypted for several years, it’s very difficult to have the IPs working because the systems are not interworking together, depending on the operator that you use. If one side is using Gmail, the other side is using Microsoft, you don’t have interworking with encryption, for instance.

    Shimel: Sure.

    Rodriguez: This is the—we try to ease the experience with this type of situation. What we see now is the remote workers as well, the employees themselves are contacting us to, “Hey, I heard about your solution. Give me some documentation that I can bring to my IT people.”

    Shimel: Well, that’s the way it always comes in. You know, it’s almost that open source mentality, but very good.

    Rodriguez: Exactly. So, that’s the number—

    Shimel: Francois, as I mentioned to you when we started, the time goes really quick, here. I apologize, but we’re way past 15 minutes.

    Rodriguez: Okay.

    Shimel: I wanna thank you for introducing Adeya to our audience. I wish you will in these uncertain times, both personally and with the company. And as I mentioned to you, maybe we could do a video interview for our TechStrong TV—

    Rodriguez: Yeah, sure.

    Shimel: And have you join us. Maybe we even could use a day or we could record the video through there or something, show people it in action.

    Rodriguez: Yeah. Okay.

    Shimel: Sounds good?

    Rodriguez: Yeah, perfect. Looking forward.

    Shimel: We’ll work on that. Alright.

    Rodriguez: Thank you, Alan.

    Shimel: Thank you.

    Rodriguez: Pleased to meet you. [Cross talk]

    Shimel: Chief growth officer for Adeya, Francois Rodriguez, here on DevOps Chat, this is Alan Shimel, and you just listened to another DevOps Chat.

    — Alan Shimel