TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits

Abstract

We introduce TinyGarble, a novel automated methodology based on powerful logic synthesis techniques for generating and optimizing compressed Boolean circuits used in secure computation, such as Yao's Garbled Circuit (GC) protocol. TinyGarble achieves an unprecedented level of compactness and scalability by using a sequential circuit description for GC. We introduce new libraries and transformations, such that our sequential circuits can be optimized and securely evaluated by interfacing with available garbling frameworks. The circuit compactness makes the memory footprint of the garbling operation fit in the processor cache, resulting in fewer cache misses and thereby less CPU cycles. Our proof-of-concept implementation of benchmark functions using TinyGarble demonstrates a high degree of compactness and scalability. We improve the results of existing automated tools for GC generation by orders of magnitude; for example, TinyGarble can compress the memory footprint required for 1024-bit multiplication by a factor of 4,172, while decreasing the number of non-XOR gates by 67%. Moreover, with TinyGarble we are able to implement functions that have never been reported before, such as SHA-3. Finally, our sequential description enables us to design and realize a garbled processor, using the MIPS I instruction set, for private function evaluation. To the best of our knowledge, this is the first scalable emulation of a general purpose processor.

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

“…In Section 3, we keep these differences in mind in order to choose the right MPC techniques for each part of the computation. Popular applications used for the empirical evaluation of practical MPC systems include graph algorithms [5,32,42,51,57], data structure algorithms [42,51], string matching and distance algorithms [36,37,66] and AES [3,36,37,66].…”

UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

“…Another family of decentralized frameworks ( [11,28,33,40,42,50]) is based on secure multiparty computation (SMPC) protocols that can theoretically perform any computation task without leaking any party's private data. Yet, several critical issues, with current SMPC solutions, render them impractical in real operational settings.…”

“…Yet, several critical issues, with current SMPC solutions, render them impractical in real operational settings. For instance, state-of-the-art SMPC libraries cannot appropriately address computations that involve more than two participants (e.g., [11,28,33,42]). The computation and memory costs of these solutions are already prohibitively high in a semi-honest adversary model, let alone when considering malicious behavior.…”

“…In the last few years, researchers have tried to address these needs by proposing different privacy-preserving solutions that enable several data providers to securely store and share their sensitive data in either a centralized or decentralized way [3,11,14,26,28,30,33,39,40,42,49,50]. Yet, despite the acknowledgment and acceptance that most of these solutions have received in the research community of privacy and security, only a few have been converted into concrete operational tools and deployed in the real world [3,14,39].…”

“…Yet, despite the acknowledgment and acceptance that most of these solutions have received in the research community of privacy and security, only a few have been converted into concrete operational tools and deployed in the real world [3,14,39]. The main reasons for such a low adoption stem from the fact that simpler solutions based on a centralized approach [30,39] only provide weakest-link security (e.g., relying on a trusted third party managing cryptographic keys); and, decentralized (and more complex) solutions based on secret sharing (SS) [3,14,26,49] or secure multiparty computation (SMPC) [11,28,33,40,42,50] have intrinsic limitations in terms of control over data and scalability. For example, SS-based solutions result in data providers losing the full control over their data as, for security reasons, they require the data storage to be outsourced to independent servers.…”