Finding Safety in Numbers with Secure Allegation Escrows

For fear of retribution, the victim of a crime may be willing to report it only if other victims of the same perpetrator also step forward. Common examples include 1) identifying oneself as the victim of sexual harassment, especially by a person in a position of authority or 2) accusing an influential politician, an authoritarian government, or ones own employer of corruption. To handle such situations, legal literature has proposed the concept of an allegation escrow: a neutral third-party that collects allegations anonymously, matches them against each other, and de-anonymizes allegers only after de-anonymity thresholds (in terms of number of co-allegers), pre-specified by the allegers, are reached. An allegation escrow can be realized as a single trusted third party; however, this party must be trusted to keep the identity of the alleger and content of the allegation private. To address this problem, this paper introduces Secure Allegation Escrows (SAE, pronounced "say"). A SAE is a group of parties with independent interests and motives, acting jointly as an escrow for collecting allegations from individuals, matching the allegations, and de-anonymizing the allegations when designated thresholds are reached. By design, SAEs provide a very strong property: No less than a majority of parties constituting a SAE can de-anonymize or disclose the content of an allegation without a sufficient number of matching allegations (even in collusion with any number of other allegers). Once a sufficient number of matching allegations exist, the join escrow discloses the allegation with the allegers' identities. We describe how SAEs can be constructed using a novel authentication protocol and a novel allegation matching and bucketing algorithm, provide formal proofs of the security of our constructions, and evaluate a prototype implementation, demonstrating feasibility in practice.Comment: To appear in NDSS 2020. New version includes improvements to writing and proof. The protocol is unchange

A Shannon Approach to Secure Multi-party Computations

In secure multi-party computations (SMC), parties wish to compute a function on their private data without revealing more information about their data than what the function reveals. In this paper, we investigate two Shannon-type questions on this problem. We first consider the traditional one-shot model for SMC which does not assume a probabilistic prior on the data. In this model, private communication and randomness are the key enablers to secure computing, and we investigate a notion of randomness cost and capacity. We then move to a probabilistic model for the data, and propose a Shannon model for discrete memoryless SMC. In this model, correlations among data are the key enablers for secure computing, and we investigate a notion of dependency which permits the secure computation of a function. While the models and questions are general, this paper focuses on summation functions, and relies on polar code constructions

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

An Effective Private Data storage and Retrieval System using Secret sharing scheme based on Secure Multi-party Computation

Privacy of the outsourced data is one of the major challenge.Insecurity of the network environment and untrustworthiness of the service providers are obstacles of making the database as a service.Collection and storage of personally identifiable information is a major privacy concern.On-line public databases and resources pose a significant risk to user privacy, since a malicious database owner may monitor user queries and infer useful information about the customer.The challenge in data privacy is to share data with third-party and at the same time securing the valuable information from unauthorized access and use by third party.A Private Information Retrieval(PIR) scheme allows a user to query database while hiding the identity of the data retrieved.The naive solution for confidentiality is to encrypt data before outsourcing.Query execution,key management and statistical inference are major challenges in this case.The proposed system suggests a mechanism for secure storage and retrieval of private data using the secret sharing technique.The idea is to develop a mechanism to store private information with a highly available storage provider which could be accessed from anywhere using queries while hiding the actual data values from the storage provider.The private information retrieval system is implemented using Secure Multi-party Computation(SMC) technique which is based on secret sharing. Multi-party Computation enable parties to compute some joint function over their private inputs.The query results are obtained by performing a secure computation on the shares owned by the different servers.Comment: Data Science & Engineering (ICDSE), 2014 International Conference, CUSA