Secret-Shared Shuffle

Chase, Melissa; Ghosh, Esha; Poburinnaya, Oxana

doi:10.1007/978-3-030-64840-4_12

Cited by 21 publications

(26 citation statements)

References 19 publications

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“…We note that these contributions are of a very different nature compared to our previous constructions, and add to the body of work on the analysis in idealized models of symmetric primitives for MPC applications [GKWY20,CT21]. Since full evaluations of PPRFs have many applications beyond PCGs, to problems such as zero-knowledge proofs [KKW18, CDG + 20, KZ20, FS21], circuit garbling [HK21], secure shuffling [CGP20] and private information retrieval [CK20, MZRA21], these results are also of independent interest.…”

Section: Our Contributionsmentioning

confidence: 95%

Correlated Pseudorandomness from Expand-Accumulate Codes

Elette

Couteau

Gilboa

et al. 2022

Lecture Notes in Computer Science

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A pseudorandom correlation generator (PCG) is a recent tool for securely generating useful sources of correlated randomness, such as random oblivious transfers (OT) and vector oblivious linear evaluations (VOLE), with low communication cost.We introduce a simple new design for PCGs based on so-called expand-accumulate codes, which first apply a sparse random expander graph to replicate each message entry, and then accumulate the entries by computing the sum of each prefix. Our design offers the following advantages compared to state-of-the-art PCG constructions:• Competitive concrete efficiency backed by provable security against relevant classes of attacks;• An offline-online mode that combines simple parallelization with a cache-friendly offline phase;• Concretely efficient extensions to pseudorandom correlation functions, which enable incremental generation of new correlation instances on demand, and to new kinds of correlated randomness that include circuit-dependent correlations.To further improve the concrete computational cost, we propose a method for speeding up a fulldomain evaluation of a puncturable pseudorandom function (PPRF). This is independently motivated by other cryptographic applications of PPRFs.

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Section: Our Contributionsmentioning

confidence: 95%

Correlated Pseudorandomness from Expand-Accumulate Codes

Elette

Couteau

Gilboa

et al. 2022

Lecture Notes in Computer Science

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“…Oblivious Shuffling. There are some works about obliviously sorting an entire array of elements in different application scenarios [17,[36][37][38]. In [17,37], the authors employed homomorphic encryption to realize a shuffling method.…”

Section: Related Workmentioning

confidence: 99%

“…There are some works about obliviously sorting an entire array of elements in different application scenarios [17,[36][37][38]. In [17,37], the authors employed homomorphic encryption to realize a shuffling method. Specifically, [17] suggests a secure shuffling protocol in a outsourced setting, which has the similar functionality of our SOSF protocol.…”

Section: Related Workmentioning

confidence: 99%

PriRanGe: Privacy-Preserving Range-Constrained Intersection Query Over Genomic Data

Yang

Yao

Weng

et al. 2023

IEEE Trans. Cloud Comput.

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Genomic data is being produced rapidly by both individuals and enterprises, and outsourcing this ever-increasing data into clouds is promising for cutting the cost of data owners and mining the wealth of genomic data at a larger scale. However, genome carries sensitive information about individuals, and it is challenging to securely and efficiently perform analysis on remotely hosted genomic databases. In this paper, we present a privacy-preserving range-constrained intersection query scheme on genomic data. To achieve security and efficiency, we propose a protocol to fulfill range-constrained intersection query, named PriRanGe. With PriRanGe, a client can securely query genomic data in a specific range in a database while keeping this whole process private. The security of our design targets genomic database confidentiality, query range/result confidentiality, and access pattern protection, and the advantage in efficiency is due to most employed primitives are symmetric. We thoroughly evaluated our design by security proof, experimental analysis and comparison to the state-of-the-art works, all of which support the conclusion that this design is both secure and fast.

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“…A random shuffle [7] of a sequence {𝑥 𝑖 } 𝑀 𝑖=1 can be implemented by two OEPs: the sequence is first permuted by a random permutation function 𝜉 0 : [𝑀] → [𝑀] generalized by Alice in private, and then by another random permutation function 𝜉 1 generated by Bob in private. Thus, the sequence is eventually permuted according to 𝜉 1 • 𝜉 0 , but neither party knows anything about this composed permutation.…”

Section: Oblivious Extended Permutation (Oep)mentioning

confidence: 99%

Secure Machine Learning over Relational Data

Luo¹,

Wang²,

Ren³

et al. 2021

Preprint

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A closer integration of machine learning and relational databases has gained steam in recent years due to the fact that the training data to many ML tasks is the results of a relational query (most often, a join-select query). In a federated setting, this poses an additional challenge, that the tables are held by different parties as their private data, and the parties would like to train the model without having to use a trusted third party. Existing work has only considered the case where the training data is stored in a flat table that has been vertically partitioned, which corresponds to a simple PK-PK join. In this paper, we describe secure protocols to compute the join results of multiple tables conforming to a general foreignkey acyclic schema, and how to feed the results in secret-shared form to a secure ML toolbox. Furthermore, existing secure ML systems reveal the PKs in the join results. We strengthen the privacy protection to higher levels and achieve zero information leakage beyond the trained model. If the model itself is considered sensitive, we show how differential privacy can be incorporated into our framework to also prevent the model from breaching individuals' privacy.

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Secret-Shared Shuffle

Cited by 21 publications

References 19 publications

Correlated Pseudorandomness from Expand-Accumulate Codes

Correlated Pseudorandomness from Expand-Accumulate Codes

PriRanGe: Privacy-Preserving Range-Constrained Intersection Query Over Genomic Data

Secure Machine Learning over Relational Data

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