Sharemind: A Framework for Fast Privacy-Preserving Computations

Abstract

Abstract. Gathering and processing sensitive data is a difficult task. In fact, there is no common recipe for building the necessary information systems. In this paper, we present a provably secure and efficient general-purpose computation system to address this problem. Our solution-SHAREMIND-is a virtual machine for privacy-preserving data processing that relies on share computing techniques. This is a standard way for securely evaluating functions in a multi-party computation environment. The novelty of our solution is in the choice of the secret sharing scheme and the design of the protocol suite. We have made many practical decisions to make large-scale share computing feasible in practice. The protocols of SHAREMIND are information-theoretically secure in the honest-but-curious model with three computing participants. Although the honest-but-curious model does not tolerate malicious participants, it still provides significantly increased privacy preservation when compared to standard centralised databases.

Secure floating point arithmetic and private satellite collision analysis

“…Sharemind [7] is an implementation of privacy-preserving computation technology based on SMC. In the most recent version of Sharemind, the users can choose which underlying SMC method suits them best.…”

“…In case of a secret-shared representation; however, access to the bits is nontrivial and involves a lot of computation for bit extraction [7,8]. An alternative would be storing the values shared bitwise, but this would render the underlying processor arithmetic unusable, and we would need to reimplement all the basic arithmetic operations on these shares.…”

“…We build the basic arithmetic primitives (addition and multiplication), develop example elementary functions (inverse, square root, exponentiation of e, error function), and present benchmarks for the implementations. As a concrete instantiation of the underlying SMC engine, we have chosen Sharemind [7], as it provides both excellent performance and convenient development tools [17]. However, all the algorithms developed are applicable for general SMC platforms in which the necessary technical routines are implemented.…”

Hybrid Model of Fixed and Floating Point Numbers in Secure Multiparty Computations

“…The respective specifications for Sharemind SMC engine and the complexities of some of the protocols can be found in [2,3,13].…”

“…There exist several frameworks allowing to work on relatively large amounts of secret-shared micro-data [2,21]. In order to obtain the homomorphic behavior needed for Turing-completeness, they work over some algebraic structure (typically, a finite ring or field).…”