Trustee: Full Privacy Preserving Vickrey Auction on top of Ethereum

The wide deployment of tokens for digital assets on top of Ethereum implies the need for powerful trading platforms. Vickrey auctions have been known to determine the real market price of items as bidders are motivated to submit their own monetary valuations without leaking their information to the competitors. Recent constructions have utilized various cryptographic protocols such as ZKP and MPC, however, these approaches either are partially privacy-preserving or require complex computations with several rounds. In this paper, we overcome these limits by presenting Trustee as a Vickrey auction on Ethereum which fully preserves bids' privacy at relatively much lower fees. Trustee consists of three components: a front-end smart contract deployed on Ethereum, an Intel SGX enclave, and a relay to redirect messages between them. Initially, the enclave generates an Ethereum account and ECDH key-pair. Subsequently, the relay publishes the account's address and ECDH public key on the smart contract. As a prerequisite, bidders are encouraged to verify the authenticity and security of Trustee by using the SGX remote attestation service. To participate in the auction, bidders utilize the ECDH public key to encrypt their bids and submit them to the smart contract. Once the bidding interval is closed, the relay retrieves the encrypted bids and feeds them to the enclave that autonomously generates a signed transaction indicating the auction winner. Finally, the relay submits the transaction to the smart contract which verifies the transaction's authenticity and the parameters' consistency before accepting the claimed auction winner. As part of our contributions, we have made a prototype for Trustee available on Github for the community to review and inspect it. Additionally, we analyze the security features of Trustee and report on the transactions' gas cost incurred on Trustee smart contract.Comment: Presented at Financial Cryptography and Data Security 2019, 3rd Workshop on Trusted Smart Contract

Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries

We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monitoring and other tasks, where the computing nodes is expected to preserve the privacy of their inputs while performing a joint computation of a certain function. Although there is a rich literature in the field of distributed systems security concerning secure multi-party computation, in practice it is hard to deploy those methods in very large scale Peer-to-Peer networks. In this work, we try to bridge the gap between theoretical algorithms in the security domain, and a practical Peer-to-Peer deployment. We consider two security models. The first is the semi-honest model where peers correctly follow the protocol, but try to reveal private information. We provide three possible schemes for secure multi-party numerical computation for this model and identify a single light-weight scheme which outperforms the others. Using extensive simulation results over real Internet topologies, we demonstrate that our scheme is scalable to very large networks, with up to millions of nodes. The second model we consider is the malicious peers model, where peers can behave arbitrarily, deliberately trying to affect the results of the computation as well as compromising the privacy of other peers. For this model we provide a fourth scheme to defend the execution of the computation against the malicious peers. The proposed scheme has a higher complexity relative to the semi-honest model. Overall, we provide the Peer-to-Peer network designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA) 200

Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare