Secure Multiparty Computation (MPC)

Protocols for secure multiparty computation (MPC) enable a set of parties to interact and compute a joint function of their private inputs while revealing nothing but the output. The potential applications for MPC are huge: privacy-preserving auctions, private DNA comparisons, private machine learning, threshold cryptography, and more. Due to this, MPC has been an intensive topic of research in academia ever since it was introduced in the 1980s by Yao for the two-party case (FOCS 1986), and by Goldreich, Micali and Wigderson for the multiparty case (STOC 1987). Recently, MPC has become efficient enough to be used in practice, and has made the transition from an object of theoretical study to a technology being used in industry. In this article, we will review what MPC is, what problems it solves, and how it is being currently used. We note that the examples and references brought in this review article are far from comprehensive, and due to the lack of space many highly relevant works are not cited

Round Optimal Secure Multiparty Computation from Minimal Assumptions

We construct a four round secure multiparty computation (MPC) protocol in the plain model that achieves security against any dishonest majority. The security of our protocol relies only on the existence of four round oblivious transfer. This culminates the long line of research on constructing round-efficient MPC from minimal assumptions (at least w.r.t. black-box simulation)

Two Round Information-Theoretic MPC with Malicious Security

We provide the first constructions of two round information-theoretic (IT) secure multiparty computation (MPC) protocols in the plain model that tolerate any $t<n/2$ malicious corruptions. Our protocols satisfy the strongest achievable standard notions of security in two rounds in different communication models. Previously, IT-MPC protocols in the plain model either required a larger number of rounds, or a smaller minority of corruptions

Round-Optimal Black-Box MPC in the Plain Model

We give the first construction of a fully black-box round-optimal secure multiparty computation (MPC) protocol in the plain model. Our protocol makes black-box use of a sub-exponentially secure two-message statistical sender private oblivious transfer (SSP-OT), which in turn can be based on (sub-exponential variants of) almost all of the standard cryptographic assumptions known to imply public-key cryptography

Privacy enhanced recommender system

Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data and generate recommendations by processing them under encryption. With this approach, the service provider learns no information on any user’s preferences or the recommendations made. The proposed method is based on homomorphic encryption schemes and secure multiparty computation (MPC) techniques. The overhead of working in the encrypted domain is minimized by packing data as shown in the complexity analysis

Efficient permutation protocol for MPC in the head

The MPC-in-the-head construction (Ishai et al., STOC\u2707) give zero-knowledge proofs from secure multiparty computation (MPC) protocols. This paper presents an efficient MPC protocol for permuting a vector of values, making use of the relaxed communication model that can be handled by the MPC-in-the-head construction. Our construction allows more efficient ZK proofs for relations expressed in the Random Access Machine (RAM) model. As a standalone application of our construction, we present batch anonymizable ring signatures

Self-Healing Computation

In the problem of reliable multiparty computation (RC), there are $n$ parties, each with an individual input, and the parties want to jointly compute a function $f$ over $n$ inputs. The problem is complicated by the fact that an omniscient adversary controls a hidden fraction of the parties. We describe a self-healing algorithm for this problem. In particular, for a fixed function $f$, with $n$ parties and $m$ gates, we describe how to perform RC repeatedly as the inputs to $f$ change. Our algorithm maintains the following properties, even when an adversary controls up to $t \leq (\frac{1}{4} - \epsilon) n$ parties, for any constant $\epsilon >0$. First, our algorithm performs each reliable computation with the following amortized resource costs: $O(m + n \log n)$ messages, $O(m + n \log n)$ computational operations, and $O(\ell)$ latency, where $\ell$ is the depth of the circuit that computes $f$. Second, the expected total number of corruptions is $O(t (\log^{*} m)^2)$, after which the adversarially controlled parties are effectively quarantined so that they cause no more corruptions.Comment: 17 pages and 1 figure. It is submitted to SSS'1

Privacy-Preserving Initial Public Offering using SCALE-MAMBA and Hyperledger Fabric

International audienceWe consider Initial Public Offering (IPO) on blockchains while preserving privacy using Secure Multiparty Computation (MPC), which allows participants to perform a computation on secret data. We provide "MPC as a service", where users requiring a computation distributes shares of their data to MPC workers who run an MPC protocol on the shares and return the result. Previous work by Benhamouda et al. considered IPO over Hyperledger Fabric. We improve by providing a tighter and easier integration of MPC protocol in Fabric using the MPC library SCALE-MAMBA. We explain the obtained security benefits and experimental results are provided