Rate-limited secure function evaluation

We introduce the notion of rate-limited secure function evaluation (RL-SFE). Loosely speaking, in an RL-SFE protocol participants can monitor and limit the number of distinct inputs (i.e., rate) used by their counterparts in multiple executions of an SFE, in a private and verifiable manner. The need for RL-SFE naturally arises in a variety of scenarios: e.g., it enables service providers to “meter” their customers’ usage without compromising their privacy, or can be used to prevent oracle attacks against SFE constructions. We consider three variants of RL-SFE providing different levels of security. As a stepping stone, we also formalize the notion of commit-first SFE (CF-SFE) wherein parties are committed to their inputs before each SFE execution. We provide compilers for transforming any CF-SFE protocol into each of the three RL-SFE variants. Our compilers are accompanied with simulation-based proofs of security in the standard model and show a clear tradeoff between the level of security offered and the overhead required. Moreover, motivated by the fact that in many client-server applications clients do not keep state, we also describe a general approach for transforming the resulting RL-SFE protocols into stateless ones. As a case study, we take a closer look at the oblivious polynomial evaluation (OPE) protocol of Hazay and Lindell, show that it is commit-first, and instantiate efficient ratelimited variants of it

IO-DSSE: Scaling Dynamic Searchable Encryption to Millions of Indexes By Improving Locality

Free cloud-based services are powerful candidates for deploying ubiquitous encryption for messaging. In the case of email and increasingly chat, users expect the ability to store and search their messages persistently. Using data from one of the top three mail providers, we confirm that for a searchable encryption scheme to scale to millions of users, it should be highly IO-efficient (locality), and handle a very dynamic message corpi. We observe that existing solutions fail to achieve both properties simultaneously. We then design, build, and evaluate a provably secure Dynamic Searchable Symmetric Encryption (DSSE) scheme with significant reduction in IO cost compared to preceding works when used for email or other highly dynamic message corpi

Fast Optimistically Fair Cut-and-Choose 2PC

Secure two party computation (2PC) is a well-studied problem with many real world applications. Due to Cleve\u27s result on general impossibility of fairness, however, the state-of-the-art solutions only provide security with abort. We investigate fairness for 2PC in presence of a trusted Arbiter, in an optimistic setting where the Arbiter is not involved if the parties act fairly. Existing fair solutions in this setting are by far less efficient than the fastest unfair 2PC. We close this efficiency gap by designing protocols for fair 2PC with covert and malicious security that have competitive performance with the state-of-the-art unfair constructions. In particular, our protocols only requires the exchange of a few extra messages with sizes that only depend on the output length; the Arbiter\u27s load is independent of the computation size; and a malicious Arbiter can only break fairness, but not covert/malicious security even if he colludes with a party. Finally, our solutions are designed to work with the state-of-the-art optimizations applicable to garbled circuits and cut-and-choose 2PC such as free-XOR, half-gates, and the cheating-recovery paradigm

ABY3: A Mixed Protocol Framework for Machine Learning

Machine learning is widely used to produce models for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection raises privacy concerns during both training and prediction stages. In this paper, we design and implement a general framework for privacy-preserving machine learning and use it to obtain new solutions for training linear regression, logistic regression and neural network models. Our protocols are in a three-server model wherein data owners secret share their data among three servers who train and evaluate models on the joint data using three-party computation (3PC). Our main contribution is a new and complete framework ABY3 for efficiently switching back and forth between arithmetic, binary, and Yao 3PC which is of independent interest. Many of the conversions are based on new techniques that are designed and optimized for the first time in this paper. We also propose new techniques for fixed-point multiplication of shared decimal values that extends beyond the three-party case, and customized protocols for evaluating piecewise polynomial functions. We design variants of each building block that is secure against malicious adversaries who deviate arbitrarily. We implement our system in C++. Our protocols are up to {\em four orders of magnitude} faster than the best prior work, hence significantly reducing the gap between privacy-preserving and plaintext training

On protection in federated social computing systems

Nowadays, a user may belong to multiple social computing systems (SCSs) in order to benefit from a variety of services that each SCS may provide. To facilitate the sharing of contents across the system boundary, some SCSs provide a mechanism by which a user may “connect ” his accounts on two SCSs. The effect is that contents from one SCS can now be shared to another SCS. Although such a connection feature delivers clear usability advantages for users, it also generates a host of privacy challenges. A notable challenge is that the access control policy of the SCS from which the content originates may not be honoured by the SCS to which the content migrates, because the latter fails to faithfully replicate the protection model of the former. In this paper we formulate a protection model for a fed-eration of SCSs that support content sharing via account connection. A core feature of the model is that sharable con-tents are protected by access control policies that transcend system boundary — they are enforced even after contents are migrated from one SCS to another. To ensure faith-ful interpretation of access control policies, their evaluation involves querying the protection states of various SCSs, us-ing Secure Multiparty Computation (SMC). An important contribution of this work is that we carefully formulate the conditions under which policy evaluation using SMC does not lead to the leakage of information about the protection states of the SCSs. We also study the computational prob-lem of statically checking if an access control policy can be evaluated without information leakage. Lastly, we identify useful policy idioms

Two-party Private Set Intersection with an Untrusted Third Party

We construct new protocols for two parties to securely compute on the items in their intersection. Our protocols make use of an untrusted third party that has no input. The use of this party allows us to construct highly efficient protocols that are secure against a single malicious corruption

Practical Privacy-Preserving K-means Clustering

Clustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacy-preserving context. Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the adaptive amortizing setting, when one needs to compute the distance from the same point to other points. This protocol can also serve as a key building block in many real-world applications such as Bio-metric Identification. Furthermore, we construct a customized garbled circuit for computing the minimum value among shared values. We implement and evaluate our protocols to demonstrate their practicality and show that they are able to train datasets that are much larger and faster than in the previous work. The numerical results also show that the proposed protocol achieve almost the same accuracy compared to a K-means plain-text clustering algorithm

Fast and Secure Three-party Computation: The Garbled Circuit Approach

Many deployments of secure multi-party computation (MPC) in practice have used information-theoretic three-party protocols that tolerate a single, semi-honest corrupt party, since these protocols enjoy very high efficiency. We propose a new approach for secure three-party computation (3PC) that improves security while maintaining practical efficiency that is competitive with traditional information-theoretic protocols. Our protocol is based on garbled circuits and provides security against a single, malicious corrupt party. Unlike information-theoretic 3PC protocols, ours uses a constant number of rounds. Our protocol only uses inexpensive symmetric-key cryptography: hash functions, block ciphers, pseudorandom generators (in particular, no oblivious transfers) and has performance that is comparable to that of Yao\u27s (semi-honest) 2PC protocol. We demonstrate the practicality of our protocol with an implementation based on the JustGarble framework of Bellare et al. (S&P 2013). The implementation incorporates various optimizations including the most recent techniques for efficient circuit garbling. We perform experiments on several benchmarking circuits, in different setups. Our experiments confirm that, despite providing a more demanding security guarantee, our protocol has performance comparable to existing information-theoretic 3PC

Efficient Zero-Knowledge Proof of Algebraic and Non-Algebraic Statements with Applications to Privacy Preserving Credentials

Practical anonymous credential systems are generally built around sigma-protocol ZK proofs. This requires that credentials be based on specially formed signatures. Here we ask whether we can instead use a standard (say, RSA, or (EC)DSA) signature that includes formatting and hashing messages, as a credential, and still provide privacy. Existing techniques do not provide efficient solutions for proving knowledge of such a signature: On the one hand, ZK proofs based on garbled circuits (Jawurek et al. 2013) give efficient proofs for checking formatting of messages and evaluating hash functions. On the other hand they are expensive for checking algebraic relations such as RSA or discrete-log, which can be done efficiently with sigma protocols. We design new constructions obtaining the best of both worlds: combining the efficiency of the garbled circuit approach for non-algebraic statements and that of sigma protocols for algebraic ones. We then discuss how to use these as building-blocks to construct privacy-preserving credential systems based on standard RSA and (EC)DSA signatures. Other applications of our techniques include anonymous credentials with more complex policies, the ability to efficiently switch between commitments (and signatures) in different groups, and secure two-party computation on committed/signed inputs