Robust Non-Interactive Multiparty Computation Against Constant-Size Collusion

Non-Interactive Multiparty Computations (Beimel et al., Crypto 2014) is a very powerful notion equivalent (under some corruption model) to garbled circuits, Private Simultaneous Messages protocols, and obfuscation. We present robust solutions to the problem of Non-Interactive Multiparty Computation in the computational and information-theoretic models. Our results include the first efficient and robust protocols to compute any function in $NC^1$ for constant-size collusions, in the information-theoretic setting and in the computational setting, to compute any function in $P$ for constant-size collusions, assuming the existence of one-way functions. Our constructions start from a Private Simultaneous Messages construction (Feige, Killian Naor, STOC 1994 and Ishai, Kushilevitz, ISTCS 1997) and transform it into a Non-Interactive Multiparty Computation for constant-size collusions. We also present a new Non-Interactive Multiparty Computation protocol for symmetric functions with significantly better communication complexity compared to the only known one of Beimel et al

Scalable and Secure Aggregation in Distributed Networks

We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-\epsilon)n$ malicious nodes for any constant $1/2 > \epsilon > 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability

Separating Two-Round Secure Computation From Oblivious Transfer

We consider the question of minimizing the round complexity of protocols for secure multiparty computation (MPC) with security against an arbitrary number of semi-honest parties. Very recently, Garg and Srinivasan (Eurocrypt 2018) and Benhamouda and Lin (Eurocrypt 2018) constructed such 2-round MPC protocols from minimal assumptions. This was done by showing a round preserving reduction to the task of secure 2-party computation of the oblivious transfer functionality (OT). These constructions made a novel non-black-box use of the underlying OT protocol. The question remained whether this can be done by only making black-box use of 2-round OT. This is of theoretical and potentially also practical value as black-box use of primitives tends to lead to more efficient constructions. Our main result proves that such a black-box construction is impossible, namely that non-black-box use of OT is necessary. As a corollary, a similar separation holds when starting with any 2-party functionality other than OT. As a secondary contribution, we prove several additional results that further clarify the landscape of black-box MPC with minimal interaction. In particular, we complement the separation from 2-party functionalities by presenting a complete 4-party functionality, give evidence for the difficulty of ruling out a complete 3-party functionality and for the difficulty of ruling out black-box constructions of 3-round MPC from 2-round OT, and separate a relaxed "non-compact" variant of 2-party homomorphic secret sharing from 2-round OT

State of the Art Report: Verified Computation

This report describes the state of the art in verifiable computation. The problem being solved is the following: The Verifiable Computation Problem (Verifiable Computing Problem) Suppose we have two computing agents. The first agent is the verifier, and the second agent is the prover. The verifier wants the prover to perform a computation. The verifier sends a description of the computation to the prover. Once the prover has completed the task, the prover returns the output to the verifier. The output will contain proof. The verifier can use this proof to check if the prover computed the output correctly. The check is not required to verify the algorithm used in the computation. Instead, it is a check that the prover computed the output using the computation specified by the verifier. The effort required for the check should be much less than that required to perform the computation. This state-of-the-art report surveys 128 papers from the literature comprising more than 4,000 pages. Other papers and books were surveyed but were omitted. The papers surveyed were overwhelmingly mathematical. We have summarised the major concepts that form the foundations for verifiable computation. The report contains two main sections. The first, larger section covers the theoretical foundations for probabilistically checkable and zero-knowledge proofs. The second section contains a description of the current practice in verifiable computation. Two further reports will cover (i) military applications of verifiable computation and (ii) a collection of technical demonstrators. The first of these is intended to be read by those who want to know what applications are enabled by the current state of the art in verifiable computation. The second is for those who want to see practical tools and conduct experiments themselves.Comment: 54 page

A Hybrid Approach to Privacy-Preserving Federated Learning

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions

Efficient cryptographic primitives: Secure comparison, binary decomposition and proxy re-encryption

”Data outsourcing becomes an essential paradigm for an organization to reduce operation costs on supporting and managing its IT infrastructure. When sensitive data are outsourced to a remote server, the data generally need to be encrypted before outsourcing. To preserve the confidentiality of the data, any computations performed by the server should only be on the encrypted data. In other words, the encrypted data should not be decrypted during any stage of the computation. This kind of task is commonly termed as query processing over encrypted data (QPED). One natural solution to solve the QPED problem is to utilize fully homomorphic encryption. However, fully homomorphic encryption is yet to be practical. The second solution is to adopt multi-server setting. However, the existing work is not efficient. Their implementations adopt costly primitives, such as secure comparison, binary decomposition among others, which reduce the efficiency of the whole protocols. Therefore, the improvement of these primitives results in high efficiency of the protocols. To have a well-defined scope, the following types of computations are considered: secure comparison (CMP), secure binary decomposition (SBD) and proxy re-encryption (PRE). We adopt the secret sharing scheme and paillier public key encryption as building blocks, and all computations can be done on the encrypted data by utilizing multiple servers. We analyze the security and the complexity of our proposed protocols, and their efficiencies are evaluated by comparing with the existing solutions.”--Abstract, page iii

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design