Non-Interactive MPC with Trusted Hardware Secure Against Residual Function Attacks

Secure multiparty computation (MPC) has been repeatedly optimized, and protocols with two communication rounds and strong security guarantees have been achieved. While progress has been made constructing non-interactive protocols with just one-round of online communication (i.e., non-interactive MPC or NI-MPC), since correct evaluation must be guaranteed with only one round, these protocols are by their nature vulnerable to the residual function attack in the standard model. This is because a party that receives a garbled circuit may repeatedly evaluate the circuit locally, while varying their own inputs and fixing the input of others to learn the values entered by other participants. We present the first MPC protocol with a one-round online phase that is secure against the residual function attack. We also present rigorous proofs of correctness and security in the covert adversary model, a reduction of the malicious model that is stronger than the semi-honest model and better suited for modeling the behaviour of parties in the real world, for our protocol. Furthermore, we rigorously analyze the communication and computational complexity of current state of the art protocols which require two rounds of communication or one-round during the online-phase with a reduced security requirement, and demonstrate that our protocol is comparable to or outperforms their complexity

Distributed Query Execution With Strong Privacy Guarantees

As the Internet evolves, we find more applications that involve data originating from multiple sources, and spanning machines located all over the world. Such wide distribution of sensitive data increases the risk of information leakage, and may sometimes inhibit useful applications. For instance, even though banks could share data to detect systemic threats in the US financial network, they hesitate to do so because it can leak business secrets to their competitors. Encryption is an effective way to preserve data confidentiality, but eliminates all processing capabilities. Some approaches enable processing on encrypted data, but they usually have security weaknesses, such as data leakage through side-channels, or require expensive cryptographic computations. In this thesis, we present techniques that address the above limitations. First, we present an efficient symmetric homomorphic encryption scheme, which can aggregate encrypted data at an unprecedented scale. Second, we present a way to efficiently perform secure computations on distributed graphs. To accomplish this, we express large computations as a series of small, parallelizable vertex programs, whose state is safely transferred between vertices using a new cryptographic protocol. Finally, we propose using differential privacy to strengthen the security of trusted processors: noise is added to the side-channels, so that no adversary can extract useful information about individual users. Our experimental results suggest that the presented techniques achieve order-of-magnitude performance improvements over previous approaches, in scenarios such as the business intelligence application of a large corporation and the detection of systemic threats in the US financial network

CoVault: A Secure Analytics Platform

In a secure analytics platform, data sources consent to the exclusive use oftheir data for a pre-defined set of analytics queries performed by a specificgroup of analysts, and for a limited period. If the platform is secure under asufficiently strong threat model, it can provide the missing link to enablingpowerful analytics of sensitive personal data, by alleviating data subjects'concerns about leakage and misuse of data. For instance, many types of powerfulanalytics that benefit public health, mobility, infrastructure, finance, orsustainable energy can be made differentially private, thus alleviatingconcerns about privacy. However, no platform currently exists that issufficiently secure to alleviate concerns about data leakage and misuse; as aresult, many types of analytics that would be in the interest of data subjectsand the public are not done. CoVault uses a new multi-party implementation offunctional encryption (FE) for secure analytics, which relies on a uniquecombination of secret sharing, multi-party secure computation (MPC), anddifferent trusted execution environments (TEEs). CoVault is secure under a verystrong threat model that tolerates compromise and side-channel attacks on anyone of a small set of parties and their TEEs. Despite the cost of MPC, we showthat CoVault scales to very large data sizes using map-reduce based queryparallelization. For example, we show that CoVault can perform queries relevantto epidemic analytics at scale.<br

Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey

With the rapid advancement of artificial intelligence technology, the usage of machine learning models is gradually becoming part of our daily lives. High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power. However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally. This has led them to explore alternative approaches such as outsourced learning and federated learning. While these methods address the feasibility of model training effectively, they introduce concerns about the trustworthiness of the training process since computations are not performed locally. Similarly, there are trustworthiness issues associated with outsourced model inference. These two problems can be summarized as the trustworthiness problem of model computations: How can one verify that the results computed by other participants are derived according to the specified algorithm, model, and input data? To address this challenge, verifiable machine learning (VML) has emerged. This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology. We first analyze the potential verifiability issues that may exist in different machine learning scenarios. Subsequently, we provide a formal definition of ZKP-VML. We then conduct a detailed analysis and classification of existing works based on their technical approaches. Finally, we discuss the key challenges and future directions in the field of ZKP-based VML

Principles of Security and Trust

This open access book constitutes the proceedings of the 8th International Conference on Principles of Security and Trust, POST 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2019. The 10 papers presented in this volume were carefully reviewed and selected from 27 submissions. They deal with theoretical and foundational aspects of security and trust, including on new theoretical results, practical applications of existing foundational ideas, and innovative approaches stimulated by pressing practical problems

CrypTen: Secure Multi-Party Computation Meets Machine Learning

Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it facilitates training of machine-learning models on private data sets owned by different parties, evaluation of one party's private model using another party's private data, etc. Although a range of studies implement machine-learning models via secure MPC, such implementations are not yet mainstream. Adoption of secure MPC is hampered by the absence of flexible software frameworks that "speak the language" of machine-learning researchers and engineers. To foster adoption of secure MPC in machine learning, we present CrypTen: a software framework that exposes popular secure MPC primitives via abstractions that are common in modern machine-learning frameworks, such as tensor computations, automatic differentiation, and modular neural networks. This paper describes the design of CrypTen and measure its performance on state-of-the-art models for text classification, speech recognition, and image classification. Our benchmarks show that CrypTen's GPU support and high-performance communication between (an arbitrary number of) parties allows it to perform efficient private evaluation of modern machine-learning models under a semi-honest threat model. For example, two parties using CrypTen can securely predict phonemes in speech recordings using Wav2Letter faster than real-time. We hope that CrypTen will spur adoption of secure MPC in the machine-learning community

GPS: Integration of Graphene, PALISADE, and SGX for Large-scale Aggregations of Distributed Data

Secure computing methods such as fully homomorphic encryption and hardware solutions such as Intel Software Guard Extension (SGX) have been applied to provide security for user input in privacy-oriented computation outsourcing. Fully homomorphic encryption is amenable to parallelization and hardware acceleration to improve its scalability and latency, but is limited in the complexity of functions it can efficiently evaluate. SGX is capable of arbitrarily complex calculations, but due to expensive memory paging and context switches, computations in SGX are bound by practical limits. These limitations make either of fully homomorphic encryption or SGX alone unsuitable for large-scale multi-user computations with complex intermediate calculations. In this paper, we present GPS, a novel framework integrating the Graphene, PALISADE, and SGX technologies. GPS combines the scalability of homomorphic encryption with the arbitrary computational abilities of SGX, forming a more functional and efficient system for outsourced secure computations with large numbers of users. We implement GPS using linear regression training as an instantiation, and our experimental results indicate a base speedup of 1.03x to 8.69x (depending on computation parameters) over an SGX-only linear regression training without multithreading or hardware acceleration. Experiments and projections show improvements over the SGX-only training of 3.28x to 10.43x using multithreading and 4.99x to 12.67 with GPU acceleration

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.Comment: Published in Foundations and Trends in Machine Learning Vol 4 Issue 1. See: https://www.nowpublishers.com/article/Details/MAL-08