NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submissio

Toward practical and private online services

Today's common online services (social networks, media streaming, messaging, email, etc.) bring convenience. However, these services are susceptible to privacy leaks. Certainly, email snooping by rogue employees, email server hacks, and accidental disclosures of user ratings for movies are some sources of private information leakage. This dissertation investigates the following question: Can we build systems that (a) provide strong privacy guarantees to the users, (b) are consistent with existing commercial and policy regimes, and (c) are affordable? Satisfying all three requirements simultaneously is challenging, as providing strong privacy guarantees usually necessitates either sacrificing functionality, incurring high resource costs, or both. Indeed, there are powerful cryptographic protocols---private information retrieval (PIR), and secure two-party computation (2PC)---that provide strong guarantees but are orders of magnitude more expensive than their non-private counterparts. This dissertation takes these protocols as a starting point and then substantially reduces their costs by tailoring them using application-specific properties. It presents two systems, Popcorn and Pretzel, built on this design ethos. Popcorn is a Netflix-like media delivery system, that provably hides, even from the content distributor (for example, Netflix), which movie a user is watching. Popcorn tailors PIR protocols to the media domain. It amortizes the server-side overhead of PIR by batching requests from the large number of concurrent users retrieving content at any given time; and, it forms large batches without introducing playback delays by leveraging the properties of media streaming. Popcorn is consistent with the prevailing commercial regime (copyrights, etc.), and its per-request dollar cost is 3.87 times that of a non-private system. The other system described in this dissertation, Pretzel, is an email system that encrypts emails end-to-end between senders and intended recipients, but allows the email service provider to perform content-based spam filtering and targeted advertising. Pretzel refines a 2PC protocol. It reduces the resource consumption of the protocol by replacing the underlying encryption scheme with a more efficient one, applying a packing technique to conserve invocations of the encryption algorithm, and pruning the inputs to the protocol. Pretzel's costs, versus a legacy non-private implementation, are estimated to be up to 5.4 times for the email provider, with additional but modest client-side requirements. Popcorn and Pretzel have fundamental connections. For instance, the cryptographic protocols in both systems securely compute vector-matrix products. However, we observe that differences in the vector and matrix dimensions lead to different system designs. Ultimately, both systems represent a potentially appealing compromise: sacrifice some functionality to build in strong privacy properties at affordable costs.Computer Science

Secure privacy-preserving computing applications on cloud using homomorphic cryptography

The advancement of cloud computing technologies has provided users and business organisations with various cloud-based options to store and access information externally, across multiple platforms and geographic locations. The cloud also has the ability to deliver scalable and high-performance computing services on demand and in a cost-effective manner while helping users to avoid the trouble of maintaining large data centres and complex computing facilities. The economies of scale increase revenue for cloud providers and lower costs for cloud users. The resulting on-demand model of computing allows providers to achieve better resource utilization through statistical multiplexing, and enables users to avoid the costs of resource over-provisioning through dynamic scaling. However, there are major security and privacy concerns when data is stored in external cloud storage systems. For example, when personal information is stored in unencrypted formats on the cloud, service providers can learn many details about the users such as their preferences, past behaviours and biometric identities. The widely distributed nature of cloud architectures means that server farms can be located in many countries or geographic locations that might be under different laws and regulations regarding user privacy. Furthermore, cloud service providers may encrypt data in-transit, but not while user data is stored on their servers, causing the reluctance of many business organisations to outsource the storage of their sensitive and valuable data, which can be major targets for attacks coming from both outside attackers and insiders. Therefore, encrypting the data when it is stored on the cloud is an important task to guarantee the confidentiality and privacy of users data. However, traditional cryptographic techniques make it difficult for processing tasks such as searching, updating or checking the integrity of encrypted data without asking clients to download and decrypt large amounts of data from the cloud. To realise the full potential of cloud computing, better cryptographic schemes are required. They should enable the cloud to perform various computing operations on encrypted data and return encrypted results to customers. Another desirable feature is how a cryptographic scheme can allow different parties to combine their encrypted data and perform some computing tasks on the cloud without compromising the confidentiality and privacy of the data of each party. Recently, homomorphic cryptography has increasingly been the focus of researchers because this technology has a great potential to provide the desirable features described above. Homomorphic encryption can be implemented either as a symmetric or a public-private asymmetric key paradigm. This technique allows many types of computing operations to be performed on ciphertext and output encrypted results which, when decrypted, are found to be identical to the results of the same operations performed on plaintext data. With a homomorphic cryptosystem, many computational circuits can now be homomorphically evaluated, producing programs that might be run on encryptions of their inputs to produce an encryption of their output. Since the inputs of such programs are encrypted, a computation task can be performed on an untrusted cloud without revealing any inputs and internal states. In this thesis, we focus the design and implementation of various application models of homomorphic cryptography so that the cloud can be used more effective and securely to store and process sensitive customer data. Our research works throughout many chapters of this thesis also provide valuable information regarding the security of homomorphic cryptography in many use case scenarios. We illustrate how homomorphic cryptography can be applied effectively with all of its flexibility, power and usefulness in many applications ranging from smart grid, e-commerce to secret sharing. In this thesis, we also propose approaches to enhance the efficiency and effectiveness of homomorphic cryptography, so that these cryptographic schemes can be applied not only in current cloud-based application, but also in larger, more mission-critical applications in the future

Electronic Voting: 6th International Joint Conference, E-Vote-ID 2021, Virtual Event, October 5–8, 2021: proceedings

This volume contains the papers presented at E-Vote-ID 2021, the Sixth International Joint Conference on Electronic Voting, held during October 5–8, 2021. Due to the extraordinary situation brought about by the COVID-19, the conference was held online for the second consecutive edition, instead of in the traditional venue in Bregenz, Austria. The E-Vote-ID conference is the result of the merger of the EVOTE and Vote-ID conferences, with first EVOTE conference taking place 17 years ago in Austria. Since that conference in 2004, over 1000 experts have attended the venue, including scholars, practitioners, authorities, electoral managers, vendors, and PhD students. The conference focuses on the most relevant debates on the development of electronic voting, from aspects relating to security and usability through to practical experiences and applications of voting systems, also including legal, social, or political aspects, amongst others, and has turned out to be an important global referent in relation to this issue

Sixth International Joint Conference on Electronic Voting E-Vote-ID 2021. 5-8 October 2021

This volume contains papers presented at E-Vote-ID 2021, the Sixth International Joint Conference on Electronic Voting, held during October 5-8, 2021. Due to the extraordinary situation provoked by Covid-19 Pandemic, the conference is held online for second consecutive edition, instead of in the traditional venue in Bregenz, Austria. E-Vote-ID Conference resulted from the merging of EVOTE and Vote-ID and counting up to 17 years since the _rst E-Vote conference in Austria. Since that conference in 2004, over 1000 experts have attended the venue, including scholars, practitioners, authorities, electoral managers, vendors, and PhD Students. The conference collected the most relevant debates on the development of Electronic Voting, from aspects relating to security and usability through to practical experiences and applications of voting systems, also including legal, social or political aspects, amongst others; turning out to be an important global referent in relation to this issue. Also, this year, the conference consisted of: · Security, Usability and Technical Issues Track · Administrative, Legal, Political and Social Issues Track · Election and Practical Experiences Track · PhD Colloquium, Poster and Demo Session on the day before the conference E-VOTE-ID 2021 received 49 submissions, being, each of them, reviewed by 3 to 5 program committee members, using a double blind review process. As a result, 27 papers were accepted for its presentation in the conference. The selected papers cover a wide range of topics connected with electronic voting, including experiences and revisions of the real uses of E-voting systems and corresponding processes in elections. We would also like to thank the German Informatics Society (Gesellschaft für Informatik) with its ECOM working group and KASTEL for their partnership over many years. Further we would like to thank the Swiss Federal Chancellery and the Regional Government of Vorarlberg for their kind support. EVote- ID 2021 conference is kindly supported through European Union's Horizon 2020 projects ECEPS (grant agreement 857622) and mGov4EU (grant agreement 959072). Special thanks go to the members of the international program committee for their hard work in reviewing, discussing, and shepherding papers. They ensured the high quality of these proceedings with their knowledge and experience

Best Practices and Recommendations for Cybersecurity Service Providers

This chapter outlines some concrete best practices and recommendations for cybersecurity service providers, with a focus on data sharing, data protection and penetration testing. Based on a brief outline of dilemmas that cybersecurity service providers may experience in their daily operations, it discusses data handling policies and practices of cybersecurity vendors along the following five topics: customer data handling; information about breaches; threat intelligence; vulnerability-related information; and data involved when collaborating with peers, CERTs, cybersecurity research groups, etc. There is, furthermore, a discussion of specific issues of penetration testing such as customer recruitment and execution as well as the supervision and governance of penetration testing. The chapter closes with some general recommendations regarding improving the ethical decision-making procedures of private cybersecurity service providers

Ethical and Unethical Hacking

The goal of this chapter is to provide a conceptual analysis of ethical, comprising history, common usage and the attempt to provide a systematic classification that is both compatible with common usage and normatively adequate. Subsequently, the article identifies a tension between common usage and a normativelyadequate nomenclature. ‘Ethical hackers’ are often identified with hackers that abide to a code of ethics privileging business-friendly values. However, there is no guarantee that respecting such values is always compatible with the all-things-considered morally best act. It is recognised, however, that in terms of assessment, it may be quite difficult to determine who is an ethical hacker in the ‘all things considered’ sense, while society may agree more easily on the determination of who is one in the ‘business-friendly’ limited sense. The article concludes by suggesting a pragmatic best-practice approach for characterising ethical hacking, which reaches beyond business-friendly values and helps in the taking of decisions that are respectful of the hackers’ individual ethics in morally debatable, grey zones