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Juliet: A Configurable Processor for Computing on Encrypted Data

IEEE TRANSACTIONS ON COMPUTERS 2024年第9期73卷 2335-2349页

作者： Gouert, Charles Mouris, Dimitris Tsoutsos, Nektarios Georgios Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU evaluation. To improve usability, we define an expressive assembly language and instruction set architecture (ISA) judiciously designed for end-to-end encrypted computation. We demonstrate Juliet's capabilities with a broad range of realistic benchmarks including cryptographic algorithms, such as the lightweight ciphers Simon and Speck, as well as logistic regression (LR) inference and matrix multiplication.

关键词： Cryptography Noise Circuits Libraries Cloud computing Computers Assembly Applied cryptography encrypted processor hardware acceleration homomorphic encryption privacy-enhancing technologies privacy-preserving ISA private computation secure cloud computing

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private Inference in Quantized Models

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第5期71卷 3957-3973页

作者： Deng, Zirui Ramkumar, Vinayak Bitar, Rawad Raviv, Netanel Washington Univ St Louis Dept Comp Sci & Engn St Louis MO 63130 USA Tel Aviv Univ Dept Elect Engn Syst IL-6997801 Tel Aviv Israel Tech Univ Munich Inst Commun Engn D-80333 Munich Germany

A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding the privacy of both parties. private Inference has been extensively explored in recent years, mainly from a cryptographic standpoint via techniques like homomorphic encryption and multiparty computation. These approaches often come with high computational overhead and may degrade the accuracy of the model. In our work, we take a different approach inspired by the private Information Retrieval literature. We view private inference as the task of retrieving inner products of parameter vectors with the data, a fundamental operation in many machine learning models. We introduce schemes that enable such retrieval of inner products for models with quantized (i.e., restricted to a finite set) weights;such models are extensively used in practice due to a wide range of benefits. In addition, our schemes uncover a fundamental tradeoff between user and server privacy. Our information-theoretic approach is applicable to a wide range of problems and robust in privacy guarantees for both the user and the server.

关键词： Information-theoretic privacy private inference private computation private computation private information retrieval private information retrieval private information retrieval

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Randomness in private Sequential Stateless Protocols 30th

Randomness in Private Sequential Stateless Protocols

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30th International Conference on the Theory and Application of Cryptology and Information Security

作者： Anilkumar, Hari Krishnan P. Narayanan, Varun Prabhakaran, Manoj Prabhakaran, Vinod M. TIFR Mumbai Maharashtra India Univ Calif Los Angeles Los Angeles CA USA Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9789819609406;9789819609413

A significant body of work in information-theoretic cryptography has been devoted to the fundamental problem of understanding the power of randomness in private computation. This has included both in-depth study of the randomness complexity of specific functions (e.g., Couteau and Rosen, ASIACRYPT 2022, gives an upper bound of 6 for n-party AND), and results for broad classes of functions (e.g., Kushilevitz et al. STOC 1996, gives an O(1) upper bound for all functions with linear-sized circuits). In this work, we make further progress on both fronts by studying randomness complexity in a new simple model of secure computation called private Sequential Stateless (PSS) model. We show that functions with O(1) randomness complexity in the PSS model are exactly those with constant-width branching programs, restricting to "speak-constant-times" protocols and to "read-constant-times" branching programs. Towards this our main construction is a novel PSS protocol for "strongly regular branching programs" (SRBP). As we show, any constant-width branching program can be converted to a constant-width SRBP, yielding one side of our characterization. The converse direction uses ideas from Kushilevitz et al. to translate randomness to communication. Our protocols are concretely efficient, has a simple structure, covers the broad class of functions with small-width, read-once (or read-a-few-times) branching programs, and hence may be of practical interest when 1-privacy is considered adequate. Also, as a consequence of our general result for SRBPs, we obtain an improvement over the protocol of Couteau and Rosen for ANDin certain cases-not in terms of the number of bits of randomness, but in terms of a simpler protocol structure (sequential, stateless).

关键词： Randomness complexity private computation private Sequential Stateless model lower bound branching programs

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Attesting Distributional Properties of Training Data for Machine Learning 29th

Attesting Distributional Properties of Training Data for Mac...

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29th European Symposium on Research in Computer Security (ESORICS)

作者： Duddu, Vasisht Das, Anudeep Khayata, Nora Yalame, Hossein Schneider, Thomas Asokan, N. Univ Waterloo Waterloo ON Canada Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9783031708787;9783031708794

The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness. Several jurisdictions are preparing ML regulatory frameworks. One such concern is ensuring that model training data has desirable distributional properties for certain sensitive attributes. For example, draft regulations indicate that model trainers are required to show that training datasets have specific distributional properties, such as reflecting the diversity of the population. We propose the novel notion of ML property attestation allowing a prover (e.g., model trainer) to demonstrate relevant properties of an ML model to a verifier (e.g., a customer) while preserving the confidentiality of sensitive data. We focus on the attestation of distributional properties of training data without revealing the data. We present an effective hybrid property attestation combining property inference with cryptographic mechanisms.

关键词： Auditing and Accountability Machine Learning Property Inference private computation

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SoK: Privacy-Preserving Computing in the Blockchain Era 7

SoK: Privacy-Preserving Computing in the Blockchain Era

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7th IEEE European Symposium on Security and Privacy (IEEE EuroS and P)

作者： Almashaqbeh, Ghada Solomon, Ravital Univ Connecticut Storrs CT 06269 USA Sunscreen San Francisco CA USA

ISBN: (纸本)9781665416146

Privacy is a huge concern for cryptocurrencies and blockchains as most of these systems log everything in the clear. This has resulted in several academic and industrial initiatives to address privacy. Starting with the UTXO model of Bitcoin, initial works brought confidentiality and anonymity to payments. Recent works have expanded to support more generalized forms of private computation. Such solutions tend to be highly involved as they rely on advanced cryptographic primitives and creative techniques to handle issues related to dealing with private records (e.g. concurrency and double spending). This situation makes it hard to comprehend the current state-of-the-art, much less build on top of it. To address these challenges, we develop a systematization of knowledge for privacy-preserving solutions in blockchain. To the best of our knowledge, our work is the first of its kind. After motivating design challenges, we devise two systematization frameworks-the first as a stepping stone to the second-and use them to study the state-of-the-art. For our first framework, we study the zero-knowledge proof systems used in surveyed solutions, based on their key features and limitations. Our second is for privacy-preserving solutions;we define several dimensions to categorize the surveyed schemes and, in doing so, identify two major paradigms employed to achieve private computation. We go on to provide insights to guide solutions' adoption and development. Finally, we touch upon challenges related to limited functionality and accommodating new developments.

关键词： Blockchain model private payments private computation zero knowledge proofs

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Information Theoretic private Inference in Quantized Models

Information Theoretic Private Inference in Quantized Models

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IEEE International Symposium on Information Theory (ISIT)

作者： Raviv, Netanel Bitar, Rawad Yaakobi, Eitan Washington Univ Dept Comp Sci & Engn St Louis MO 14263 USA Tech Univ Munich Inst Comp Engn Munich Germany Technion Israel Inst Technol Dept Comp Sci Haifa Israel

ISBN: (纸本)9781665421607;9781665421591

In a private Inference scenario, a server holds a model (e.g., a neural network), a user holds data, and the user wishes to apply the model on her data. The privacy of both parties must be protected;the user's data might contain confidential information, and the server's model is his intellectual property. private inference has been studied extensively in recent years, mostly from a cryptographic perspective by incorporating homomorphic encryption and multiparty computation protocols, which incur high computational overhead and degrade the accuracy of the model. In this work we take a perpendicular approach which draws inspiration from the expansive private Information Retrieval literature. We view private inference as the task of retrieving an inner product of a parameter vector with the data, a fundamental step in most machine learning models. By combining binary arithmetic with real-valued one, we present a scheme which enables the retrieval of the inner product for models whose weights are either binarized, or given in fixed-point representation;such models gained increased attention recently, due to their ease of implementation and increased robustness. We also present a fundamental trade-off between the privacy of the user and that of the server, and show that our scheme is optimal in this sense. Our scheme is simple, universal to a large family of models, provides clear information-theoretic guarantees to both parties with zero accuracy loss, and in addition, is compatible with continuous data distributions and allows infinite precision.

关键词： private inference private computation private information retrieval

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Homomorphic Sorting With Better Scalability

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第4期32卷 760-771页

作者： Cetin, Gizem S. Savas, Erkay Sunar, Berk Worcester Polytech Inst Dept Elect & Comp Engn Worcester MA 01609 USA Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey

Homomorphic sorting is an operation that blindly sorts a given set of encrypted numbers without decrypting them (thus, there is no need for the secret key). In this article, we propose a new, efficient, and scalable method for homomorphic sorting of numbers: polynomial rank sort algorithm. To put the new algorithm in a comparative perspective, we provide an extensive survey of classical sorting algorithms and networks that are not directly suitable for homomorphic computation. We also include, in our discussions, two of our previous algorithms specifically designed for homomorphic sorting operation: direct and greedy sort, and explain how they evolve from classical sorting networks. We theoretically show that the new algorithm is superior in terms of multiplicative depth when compared with all other algorithms. When batched implementation is used, the number of comparisons is reduced from O(N-2) to O(N) provided that the number of slots is larger than or equal to the number of elements in the set. Our software implementation results confirm that the new algorithm is several orders of magnitude faster than many methods in the literature. Also, the polynomial sort algorithm scales better than the fastest algorithm in the literature to the best our knowledge although for small sets the execution times are comparable. The proposed algorithm is amenable to parallel implementation as most time consuming operations in the algorithm can naturally be performed concurrently.

关键词： private computation encrypted computing fully homomorphic encryption homomorphic sorting

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Work-In-Progress: private Runtime Verification

Work-In-Progress: Private Runtime Verification

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Embedded Systems Week / Int Conf on Compilers, Architecture, and Synthesis for Embedded Systems (CASES) / International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) / Int Conf on Embedded Software (EMSOFT)

作者： Abbas, Houssam Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781450369244

Complex systems require runtime monitoring of their operation, and this task can be performed by a separate monitoring process. A hacked monitor however could use the system traces it receives to attack the system or leak the information to competitors. We introduce private runtime verification, in which the monitoring process learns nothing about the traces, and yet is still able to correctly monitor them for satisfaction of temporal specifications.

关键词： private computation runtime verification cyber-physical systems

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Applications of Fully Homomorphic Encryption

Applications of Fully Homomorphic Encryption

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作者： Gizem S. Cetin Worcester Polytechnic Institute

学位级别：博士

Homomorphic encryption has progressed rapidly in both efficiency and versatility since its emergence in 2009. Meanwhile, a multitude of pressing privacy needs --- ranging from cloud computing to healthcare management to the handling of shared databases such as those containing genomics data --- call for immediate solutions that apply fully homomorpic encryption (FHE) and somewhat homomorphic encryption (SHE) technologies. Recent rapid progress in fully homomorphic encryption has catalyzed renewed efforts to develop efficient privacy preserving protocols. Several works have already appeared in the literature that provide solutions to these problems by employing leveled or somewhat homomorphic encryption techniques. Here, we propose efficient ways of adapting the most fundamental programming problems; boolean algebra, arithmetic in binary and higher radix representation, sorting, and search to the fully homomorphic encryption domain by focusing on the multiplicative depth of the circuits alongside the more traditional metrics. The reduced depth allows much reduced noise growth and thereby makes it possible to select smaller parameter sizes in leveled FHE instantiations resulting in greater efficiency savings. We begin by exploring already existing solutions to these programming problems, and analyze them in terms of homomorphic evaluation and memory costs. Most of these algorithms appear to be not the best candidates for FHE solutions, hence we propose new methods and improvements over the existing algorithms to optimize performance.

关键词： encrypted computing fully homomorphic encryption private computation

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Ryoan: A Distributed Sandbox for Untrusted computation on Secret Data

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ACM TRANSACTIONS ON COMPUTER SYSTEMS 2018年第4期35卷 13-13页

作者： Hunt, Tyler Zhu, Zhiting Xu, Yuanzhong Peter, Simon Witchel, Emmett Univ Texas Austin Comp Sci Dept 2317 SpeedwayStop D9500 Austin TX 78712 USA Google 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan1 protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult, because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e. g., Intel's software guard extensions (SGX) [40]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user's input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, health analysis, image processing and machine translation.

关键词： Intel SGX enclaves sandboxing untrusted OS private computation

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