Privacy Preserving Computation in Home Loans using the FRESCO Framework

Secure Multiparty Computation (SMC) is a subfield of cryptography that allows multiple parties to compute jointly on a function without revealing their inputs to others. The technology is able to solve potential privacy issues that arises when a trusted third party is involved, like a server. This paper aims to evaluate implementations of Secure Multiparty Computation and its viability for practical use. The paper also seeks to understand and state the challenges and concepts of Secure Multiparty Computation through the construction of a home loan calculation application. Encryption over MPC is done within 2 to 2.5 Seconds. Up to 10K addition operations, MPC system performs very well and most applications will be sufficient within 10K additions

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)

Efficient Multiparty Computations with Dishonest Minority

We consider verifiable secret sharing (VSS) and multiparty computation (MPC) in the secure channels model, where a broadcast channel is given and a non-zero error probability is allowed. In this model Rabin and Ben-Or proposed VSS and MPC protocols, secure against an adversary that can corrupt any minority of the players. In this paper, we rst observe that a subprotocol of theirs, known as weak secret sharing (WSS), is not secure against an adaptive adversary, contrary to what was believed earlier. We then propose new and adaptively secure protocols for WSS, VSS and MPC that are substantially more efficient than the original ones. Our protocols generalize easily to provide security against general Q2 adversaries

A New Approach to Round-Optimal Secure Multiparty Computation

We present a new approach towards constructing round-optimal secure multiparty computation (MPC) protocols against malicious adversaries without trusted setup assumptions. Our approach builds on ideas previously developed in the context of covert multiparty computation [Chandran et al., FOCS\u2707] even though we do not seek covert security. Using our new approach, we obtain the following results: 1. A five round MPC protocol based on the Decisional Diffie-Hellman (DDH) assumption. 2. A four round MPC protocol based on one-way permutations and sub-exponentially secure DDH. This result is {\em optimal} in the number of rounds. Previously, no four-round MPC protocol for general functions was known and five-round protocols were only known based on indistinguishability obfuscation (and some additional assumptions) [Garg et al., EUROCRYPT\u2716]

Scalable Multiparty Garbling

Multiparty garbling is the most popular approach for constant-round secure multiparty computation (MPC). Despite being the focus of significant research effort, instantiating prior approaches to multiparty garbling results in constant-round MPC that can not realistically accommodate large numbers of parties. In this work we present the first global-scale multiparty garbling protocol. The per-party communication complexity of our protocol decreases as the number of parties participating in the protocol increases---for the first time matching the asymptotic communication complexity of non-constant round MPC protocols. Our protocol achieves malicious security in the honest-majority setting and relies on the hardness of the Learning Party with Noise assumption

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange