Wysteria: A Programming Language for Generic, Mixed-Mode Multiparty Computations

Rastogi, Aseem; Hammer, Matthew A.; Hicks, Michael

doi:10.1109/sp.2014.48

Cited by 96 publications

(87 citation statements)

References 27 publications

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“…However, they do not consider verified implementations. Wysteria [53] is a new programming language for mixed-mode multiparty computations. Its design is supported by a rigorous pen-and-paper proof that typable programs do not leak information in unintended ways.…”

Section: High-assurance Cryptographymentioning

confidence: 99%

A Fast and Verified Software Stack for Secure Function Evaluation

Almeida

Barbosa

Barthe

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

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We present a high-assurance software stack for secure function evaluation (SFE). Our stack consists of three components: i. a verified compiler (CircGen) that translates C programs into Boolean circuits; ii. a verified implementation of Yao's SFE protocol based on garbled circuits and oblivious transfer; and iii. transparent application integration and communications via FRESCO, an open-source framework for secure multiparty computation (MPC). CircGen is a general purpose tool that builds on CompCert, a verified optimizing compiler for C. It can be used in arbitrary Boolean circuit-based cryptography deployments. The security of our SFE protocol implementation is formally verified using EasyCrypt, a tool-assisted framework for building high-confidence cryptographic proofs, and it leverages a new formalization of garbled circuits based on the framework of Bellare, Hoang, and Rogaway (CCS 2012). We conduct a practical evaluation of our approach, and conclude that it is competitive with state-of-the-art (unverified) approaches. Our work provides concrete evidence of the feasibility of building efficient, verified, implementations of higher-level cryptographic systems. All our development is publicly available.

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Section: High-assurance Cryptographymentioning

confidence: 99%

A Fast and Verified Software Stack for Secure Function Evaluation

Almeida

Barbosa

Barthe

et al. 2017

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

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“…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.…”

Section: Related Workmentioning

confidence: 99%

“…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].…”

mentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

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UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Froelicher

Egger

Sousa

et al. 2017

Proceedings on Privacy Enhancing Technologies

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Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacypreserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.

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“…Secure multi-party computation (MPC) resolves this tension: aggregate data may be computed while protecting each contributor's confidentiality. Theoretical constructs have been known for decades [1]- [3] and recent efforts aim to deliver them to end-users [4]- [6].…”

Section: Introductionmentioning

confidence: 99%