Optimally Efficient Secure Scalar Product With Applications in Cloud Computing

Siabi, Babak; Berenjkoub, Mehdi; Susilo, Willy

doi:10.1109/access.2019.2908230

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…Its implementation in a privacy-preserving setting remains expensive as it requires either generating Beaver triples [5] or using fully homomorphic encryption (FHE). There is a long list of results for secure computation of dot product or linear algebra in general [1,4,14,24,42,46,47]. For the applications that we consider in this paper, namely, Covid-heatmap and ARL, dot-product of sparse vectors would be sufficient.…”

Section: Secure Dot Product and Its Applicationsmentioning

confidence: 99%

Multiparty Private Set Intersection Cardinality and Its Applications

Gao,

Trieu,

Yanai

2024

PoPETs

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We describe a new paradigm for multi-party private set intersection cardinality (PSI-CA) that allows $n$ parties to compute the intersection size of their datasets without revealing any additional information. We explore a variety of instantiations of this paradigm. By operating under the assumption that a particular subset of parties refrains from collusion, our protocols avoid computationally expensive public-key operations and are secure in the presence of a semi-honest adversary. We demonstrate the practicality of our PSI-CA with an implementation. For $n=16$ parties with data-sets of $2^{20}$ items each, our server-aided variant takes 71 seconds. Interestingly, in the server-less setting, the same task takes only 7 seconds. To the best of our knowledge, this is the first `special purpose' implementation of a multi-party PSI-CA from symmetric-key techniques (i.e. an implementation that does not rely on a generic underlying MPC).We study two interesting applications -- heatmap computation and associated rule learning (ARL) -- that can be computed securely using a dot-product as a building block. We analyse the performance of securely computing heatmap and ARL using our protocol and compare that to the state-of-the-art.

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Section: Secure Dot Product and Its Applicationsmentioning

confidence: 99%

Multiparty Private Set Intersection Cardinality and Its Applications

Gao,

Trieu,

Yanai

2024

PoPETs

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“…In Refs. [2,31,32], those protocols add random quantities to protect the privacy of vectors instead of using encryption scheme. The protocol designed by Zhu et al [31] can efficiently compute scalar product of even dimensions, but it requires a secure channel and will leak partial sums of private data.…”

Section: Introductionmentioning

confidence: 99%

“…Secure multi‐party computation (SMC) was first proposed by Yao in 1982 [ 1 ] . It is an important research direction of modern cryptography, and a powerful tool for handling sensitive distributed computing problems [ 2 ] . In SMC, mutually distrusted participants

P_{1}, P_{2}, \dots, P_{n}

input their own private data

x_{i}

to jointly compute a function

(y_{1}, y_{2}, \dots, y_{n}) = f (x_{1}, x_{2}, \dots, x_{n})

.…”

Section: Introductionmentioning

confidence: 99%

Secure Scalar Product Protocols

Mengyu

2021

Chinese J of Electronics

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Secure scalar product computation is a special secure multi‐party computation problem. A secure scalar product protocol can be used by two parties to jointly compute the scalar product of their private vectors without revealing any information about the private vector of either party. Secure scalar product protocol is of great significance in privacy‐preserving scientific computing, privacy preserving data mining, privacy‐preserving cooperative statistical analysis and privacy‐preserving geometry computation, etc. Many privacy preserving computing problems can be transformed to secure scalar product computation. At present, existing scalar product protocols cannot be used to privately compute scalar product of private vectors with both positive and negative components. Based on homomorphic encryption scheme, we design three protocols to compute scalar product of three different kinds of private vectors. The components of the first kind vector are arbitrary integers; those of the second kind are positive rational numbers and those of the third kind are arbitrary rational numbers. We use simulation paradigm proving that the protocols are secure in the semi‐honest model. Theoretical analysis and experimental results show that the protocols designed in this paper are efficient.

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Privacy-preserving content-based image retrieval in edge environment

Yan

Zhang

et al. 2021

Cluster Comput

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Optimally Efficient Secure Scalar Product With Applications in Cloud Computing

Cited by 4 publications

References 53 publications

Multiparty Private Set Intersection Cardinality and Its Applications

Multiparty Private Set Intersection Cardinality and Its Applications

Secure Scalar Product Protocols

Privacy-preserving content-based image retrieval in edge environment

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