Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation

Boyle, Elette; Chandran, Nishanth; Gilboa, Niv; Gupta, Divya; Ishai, Yuval; Kumar, Nishant; Rathee, Mayank

doi:10.1007/978-3-030-77886-6_30

Cited by 54 publications

(68 citation statements)

References 40 publications

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“…On the other hand, the recent studies by Boyle et al [300,301] proposed a new approach to construct mixed-mode MPC protocols based on function secret sharing (FSS), which is useful for ML applications with optimal online communication and round complexity. Their FSS approach supports arithmetic operations that are mixed with non-arithmetic operations.…”

Section: Mpc Application To Machine Learningmentioning

confidence: 99%

“…For online communication cost and rounds, the FSS-based approach outperforms the GC and GMW approaches. Their FSS-based approach can be also secure against malicious adversaries [300]. For now, Boyle et al [300,301] only proposed efficient constructions for functions including comparison (e.g., ReLU), splines (e.g., used in sigmoid), bit-decomposition, zero test, and arithmetic/logical shifts.…”

Section: Mpc Application To Machine Learningmentioning

confidence: 99%

“…Their FSS-based approach can be also secure against malicious adversaries [300]. For now, Boyle et al [300,301] only proposed efficient constructions for functions including comparison (e.g., ReLU), splines (e.g., used in sigmoid), bit-decomposition, zero test, and arithmetic/logical shifts. There are still many functions used in ML and scientific computation (e.g., exponentiation, tanh, and reciprocal of square root), whose concretely efficient FSS-based 2PC protocols are unknown.…”

Section: Mpc Application To Machine Learningmentioning

confidence: 99%

“…There are still many functions used in ML and scientific computation (e.g., exponentiation, tanh, and reciprocal of square root), whose concretely efficient FSS-based 2PC protocols are unknown. Besides, Boyle et al [300,301] only gave two-party constructions. It is an interesting future work to construct concretely efficient FSSbased MPC protocols with optimal online communication and rounds for multiple parties (i.e., n ≥ 3).…”

Section: Mpc Application To Machine Learningmentioning

confidence: 99%

See 3 more Smart Citations

Concretely efficient secure multi-party computation protocols: survey and more

Feng

Yang

2022

Security and Safety

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Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on their private inputs, and reveals nothing but the output of the function. In the last decade, MPC has rapidly moved from a purely theoretical study to an object of practical interest, with a growing interest in practical applications such as privacy-preserving machine learning (PPML). In this paper, we comprehensively survey existing work on concretely efficient MPC protocols with both semi-honest and malicious security, in both dishonest-majority and honest-majority settings. We focus on considering the notion of security with abort, meaning that corrupted parties could prevent honest parties from receiving output after they receive output. We present high-level ideas of the basic and key approaches for designing different styles of MPC protocols and the crucial building blocks of MPC. For MPC applications, we compare the known PPML protocols built on MPC, and describe the efficiency of private inference and training for the state-of-the-art PPML protocols. Furthermore, we summarize several challenges and open problems to break though the efficiency of MPC protocols as well as some interesting future work that is worth being addressed. This survey aims to provide the recent development and key approaches of MPC to researchers, who are interested in knowing, improving, and applying concretely efficient MPC protocols.

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Section: Mpc Application To Machine Learningmentioning

confidence: 99%

Section: Mpc Application To Machine Learningmentioning

confidence: 99%

Section: Mpc Application To Machine Learningmentioning

confidence: 99%

Section: Mpc Application To Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

Concretely efficient secure multi-party computation protocols: survey and more

Feng

Yang

2022

Security and Safety

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“…However, simply applying ASS over each user's complete vector is inefficient because social graphs are usually large-scale and sparse [14], leading to many zero elements in local view vectors that incur undesirable performance overheads. To tackle this problem, PrivGED develops techniques that allow to exploit the benefits of graph sparsity for efficiency while protecting the privacy of users' private social relationships, through a delicate synergy of sparse representation, local differential privacy (LDP) [4], and function secret sharing (FSS) [17], [18] techniques. As opposed to PrivateGraph [8], PrivGED does not reveal any users' exact node degree information.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Analytics on Decentralized Social Graphs: The Case of Eigendecomposition

Wang¹,

Zheng²,

Jia³

et al. 2022

Preprint

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Analytics over social graphs allows to extract valuable knowledge and insights for many fields like community detection, fraud detection, and interest mining. In practice, decentralized social graphs frequently arise, where the social graph is not available to a single entity and is decentralized among a large number of users, each holding only a limited local view about the whole graph. Collecting the local views for analytics of decentralized social graphs raises critical privacy concerns, as they encode private information about the social interactions among individuals. In this paper, we design, implement, and evaluate PrivGED, a new system aimed at privacy-preserving analytics over decentralized social graphs. PrivGED focuses on the support for eigendecomposition, one popular and fundamental graph analytics task producing eigenvalues/eigenvectors over the adjacency matrix of a social graph and benefits various practical applications. PrivGED is built from a delicate synergy of insights on graph analytics, lightweight cryptography, and differential privacy, allowing users to securely contribute their local views on a decentralized social graph for a cloud-based eigendecomposition analytics service while gaining strong privacy protection. Extensive experiments over real-world social graph datasets demonstrate that PrivGED achieves accuracy comparable to the plaintext domain, with practically affordable performance superior to prior art.

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