High-performance secure multi-party computation for data mining applications

Bogdanov, Dan; Niitsoo, Margus; Toft, Tomas; Willemson, Jan

doi:10.1007/s10207-012-0177-2

Cited by 173 publications

(151 citation statements)

References 10 publications

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“…For more details on the security model of Sharemind, please refer to the PhD thesis [6]. Note that since then, the primitive protocols of Sharemind have been updated to be more efficient [8].…”

Section: The Sharemind Platformmentioning

confidence: 99%

“…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.…”

Section: Representation Of Floating Point Numbersmentioning

confidence: 99%

“…Throughout this and several following protocols, we will make use of primitives described in [8]. Recall, that we do not check for any exponent under-or overflows during multiplication.…”

Section: Multiplicationmentioning

confidence: 99%

“…First, we compute all the possible n rightshifted values in parallel. Next, we extract the bits of the shift using the BitExtr routine from [8]. Recall that p ∈ [0, n −1], so we obtain log 2 (n) bits.…”

Section: Additionmentioning

confidence: 99%

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Secure floating point arithmetic and private satellite collision analysis

Kamm

Willemson

2014

Int. J. Inf. Secur.

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In this paper, we show that it is possible and, indeed, feasible to use secure multiparty computation (SMC) for calculating the probability of a collision between two satellites. For this purpose, we first describe basic floating point arithmetic operators (addition and multiplication) for multiparty computations. The operators are implemented on the Sharemind SMC engine. We discuss the implementation details, provide methods for evaluating example elementary functions (inverse, square root, exponentiation of e, error function). Using these primitives, we implement a satellite conjunction analysis algorithm and give benchmark results for the primitives as well as the conjunction analysis itself.

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Section: The Sharemind Platformmentioning

confidence: 99%

Section: Representation Of Floating Point Numbersmentioning

confidence: 99%

“…Throughout this and several following protocols, we will make use of primitives described in [8]. Recall, that we do not check for any exponent under-or overflows during multiplication.…”

Section: Multiplicationmentioning

confidence: 99%

Section: Additionmentioning

confidence: 99%

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Secure floating point arithmetic and private satellite collision analysis

Kamm

Willemson

2014

Int. J. Inf. Secur.

Self Cite

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“…While the generic construction is expensive in computation and communication, the result has sparked research activities in secure multiparty computation (SMC), with results that are impressive both performance-wise [9,11,17,20], as well as in the variety of concrete problems that have been tackled [10,14,16,21]. From the start, two kinds of adversaries -passive and active -have been considered in the construction of SMC protocols, with highest performance and the greatest variety achieved for protocol sets secure only against passive adversaries.…”

Section: Introductionmentioning

confidence: 99%

Verifiable Computation in Multiparty Protocols with Honest Majority

Laud

Pankova

2014

Provable Security

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Abstract. We present a generic method for turning passively secure protocols into protocols secure against covert attacks. The method adds a post-execution verification phase to the protocol that allows a misbehaving party to escape detection only with negligible probability. The execution phase, after which the computed protocol result is already available for parties, has only negligible overhead added by our method. The checks, based on linear probabilistically checkable proofs, are done in zero-knowledge, thereby preserving the privacy guarantees of the original protocol. Our method is inspired by recent results in verifiable computation, adapting them to multiparty setting and significantly lowering their computational costs for the provers.

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OIDPR: Optimized insulin dosage via privacy‐preserving reinforcement learning

Ying

Zhang

Cao

et al. 2020

Trans Emerging Tel Tech

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The precision of insulin dosage is essential in the process of diabetes treatment. The fact is providing precise dosage is almost impossible for clinicians since blood sugar levels are dynamically affected by many factors. Even though some auxiliary dosing systems have been proposed, the required real‐time physical data about the health situation of diabetics is still hard to synchronize to the end‐devices instantly. The traditional personalized drug delivery frameworks for accurate dosing of insulin always collect and transmit medical data in cleartext, which raises privacy problems. In this article, we propose a framework for an optimized insulin dosage via privacy‐preserving reinforcement learning to diabetics (OIDPR). In OIDPR, both the additive secret sharing and edge computing are deployed to achieve data confidentiality and performance optimization. The medical data is divided into multiple secret shares uniformly at random for outsourcing and operating at the edge servers. During the computation task of reinforcement learning, data is encrypted and processed via our proposed additive secret sharing protocol, where the privacy is reserved by the efficient encryption mechanism and the secret sharing system only incurs little workload. We provide comprehensive theoretical analyses and experimental results that demonstrate the supervisor functionality and high performance of our framework.

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High-performance secure multi-party computation for data mining applications

Cited by 173 publications

References 10 publications

Secure floating point arithmetic and private satellite collision analysis

Secure floating point arithmetic and private satellite collision analysis

Verifiable Computation in Multiparty Protocols with Honest Majority

OIDPR: Optimized insulin dosage via privacy‐preserving reinforcement learning

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