Fast Secure Scalar Product Protocol with (almost) Optimal Efficiency

Zhu, Youwen; Wang, Zhikuan; Hassan, Bilal; Zhang, Yue; Wang, Jian; Qian, Cheng

doi:10.1007/978-3-319-28910-6_21

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…In fact, it has been proved that it is not possible to have secure two-party computation without relying on intractable problems or relaxed computation models [4]. In light of this general fact, we can conclude that the attempts of [36], [37], [38] and [39] have been condemned to failure from the beginning. Therefore, we do not discuss such solutions further.…”

Section: B Related Workmentioning

confidence: 99%

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Optimally Efficient Secure Scalar Product With Applications in Cloud Computing

2019

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Secure computation of scalar product is of considerable importance due to its central role in many practical computation scenarios with privacy and security requirements. This paper includes new results about the secure two-party scalar product. Specifically, a perfectly secure and universally composable two-party split scalar product (SSP) protocol is proposed in the preprocessing model. In addition to full security, the proposed SSP protocol enjoys the advantage of optimal efficiency. To show the optimality of this SSP protocol, information theoretic lower bounds on the amount of communicated data in a secure two-party computation in the preprocessing model are derived. These bounds are not limited to SSP functionality but apply to a large class of two-party functionalities. A part of this paper is devoted to applications of the proposed SSP protocol in secure cloud computing. Specifically, based on this protocol, a cloud-assisted privacy-preserving profile-matching scheme and a secure remote health monitoring scheme are proposed. Both of the solutions are highly efficient and significantly improve the previous work. INDEX TERMSSecure computations, preprocessing model, optimal efficiency, privacy-preserving scalar product, privacy-preserving profile-matching, secure remote health monitoring.

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Section: B Related Workmentioning

confidence: 99%

“…There are several attempts at secure realization of scalar product functionalities in the literature. In [36], [37], [38] and [39], the two-party SSP functionality is considered and solutions without relying on a third party or intractable problems are targeted. Unfortunately, all of these solutions trade off security for efficiency.…”

Section: B Related Workmentioning

confidence: 99%

Optimally Efficient Secure Scalar Product With Applications in Cloud Computing

2019

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“…Nevertheless, the schemes either are not provably secure or have heavy computation and communication overheads. Recently, Zhu et al propose two efficient solutions for secure scalar product protocol [45,46], which can be utilized to securely compute the Hamming distance of two private strings but cannot support the distance comparison. Cheng et al [47] review the approaches to secure Internet of Things in a quantum world.…”

Section: Related Workmentioning

confidence: 99%

FTP: An Approximate Fast Privacy-Preserving Equality Test Protocol for Authentication in Internet of Things

Zhu

Zhang

Yuan

et al. 2018

Security and Communication Networks

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Privacy-preserving string equality test is a fundamental operation of many algorithms, including privacy-preserving authentication in Internet of Things (IoT). Existing secure equality test schemes can theoretically achieve string equality comparison and preserve the private strings. However, they suffer from heavy computation and communication cost, especially while the strings are of hundreds of bits or longer, which is not suitable for IoT applications. In this paper, we propose an approximate Fast privacy-preserving equality Test Protocol (FTP), which can securely complete string equality test and achieve high running efficiency at the cost of little accuracy loss. We strictly analyze the accuracy of our proposed scheme and formally prove its security. Additionally, we leverage extensive simulation experiments to evaluate the running cost, which confirms our high efficiency; for instance, our proposed FTP can securely compare two 256-bit strings within 0.7 seconds on ordinary laptops.

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“…Their protocol was based on Homomorphic encryption to protect privacy under semi-honest model of adversaries. Zhu et al [29] proposed a secure two-party scalar product protocol. Authors affirmed having no extra communication overheads compared to the scalar product computation without privacy-protection.…”

Section: Privacy-preserving Cosine Similaritymentioning

confidence: 99%

“…To summarize, we can classify existing privacy-preserving similarity evaluation works into two categories: a) methods that are based on cryptographic schemes [25,30,31,32,26,27,28,29], such as Homomorphic encryption. These methods are trading security and performance.…”

Section: Privacy-preserving Cosine Similaritymentioning

confidence: 99%

Efficient and privacy-aware multi-party classification protocol for human activity recognition

Gheid

Challal

et al. 2017

Journal of Network and Computer Applications

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Human activity recognition (HAR) is an important research field that relies on sensing technologies to enable many context-aware applications. Nevertheless, tracking personal signs to enable such applications has given rise to serious privacy issues, especially when using external activity recognition services. In this paper, we propose (Π-Knn): a privacy-preserving version of the K Nearest Neighbors (k-NN) classifier that is mainly built on (Π-CSP+): a novel cryptography-free private similarity evaluation protocol. As a sample application, we consider a medical monitoring system enhanced with a HAR process based on our privacy preserving classifier. The integration of the privacy preserving HAR aims to improve the accuracy of the clinical decision support. We conduct a standard security analysis to prove that our protocols provide a complete privacy protection against malicious adversaries. We perform a comparative performance evaluation through several experiments while using real HAR system parameters. Experimental evaluations show that our protocol (Π-CSP+) incurs a low increasing overhead (37% in Online classification and 50% in Offline classification) compared to PCSC, representative state-of-the art protocol, which incurs 3600% and 4800% in online and offline classification respectively. Besides, Π-CSP+ provides a stable and efficient response time (W =0.0x m.seconds) for both short and long duration activities while serving up to 1000 clients. Comparative results confirm the computational efficiency of our protocol against a competitive state-of-the-art protocol.

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Fast Secure Scalar Product Protocol with (almost) Optimal Efficiency

Cited by 5 publications

References 18 publications

Optimally Efficient Secure Scalar Product With Applications in Cloud Computing

Optimally Efficient Secure Scalar Product With Applications in Cloud Computing

FTP: An Approximate Fast Privacy-Preserving Equality Test Protocol for Authentication in Internet of Things

Efficient and privacy-aware multi-party classification protocol for human activity recognition

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