Efficient Solutions to Two-Party and Multiparty Millionaires’ Problem

Liu, Xin; Li, Shundong; Chen, Xiu-Bo; Xu, Gang; Zhang, Xiaolin; Zhou, Yong

doi:10.1155/2017/5207386

Cited by 19 publications

(16 citation statements)

References 32 publications

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“…1) Motivated by the vectorization method proposed in [8], we transform the private integer comparison into a block comparison problem. We encode the private inputs into blocks to improve the protocol efficiency for large integers comparison.…”

Section: A Our Contributionsmentioning

confidence: 99%

“…2) We propose an efficient two-party integer comparison protocol in the semi-honest model. By using a block vectorization mechanism, our protocol significantly reduces the execution time of the protocol in [8] and achieves low communication and computational complexities for large integer inputs. The computational complexity of our protocol is 2( √ M +2) lg N modular multiplications, where M is the size of the original encoding vector.…”

Section: A Our Contributionsmentioning

confidence: 99%

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Efficient Two-Party Integer Comparison With Block Vectorization Mechanism

2021

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Private integer comparison has been an essential computation function for many applications, including online auction, credential identification, data mining, and joint bidding. In the setting of two-party computation, two parties with private inputs (x and y) want to jointly compare them without revealing the value of those inputs to others (also known as the Millionaires' problem) while the output should ensure correctness and preserve data privacy. The private inputs only can be revealed if they are equal, i.e., x = y. Many related works have been proposed to solve the integer comparison problem in various settings, focusing on different properties such as round and computation complexity. Most solutions decompose integers into bitwise representation and then securely evaluate the function in a Boolean circuit on encrypted bits. However, this type of solution is costly (especially for large integers) as each bit requires encryption and decryption. In this paper, we transform the private integer comparison into a block comparison problem. In particular, we employ a block vectorization mechanism to encode the private inputs into blocks. We show the security of our two-party protocol in the semi-honest model. Also, we implement the protocol to demonstrate its efficiency using block vectorization mechanism and homomorphic encryption. The experimental result proves that our proposed solution achieves high efficiency, particularly for large integer comparisons.

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Section: A Our Contributionsmentioning

confidence: 99%

Section: A Our Contributionsmentioning

confidence: 99%

Efficient Two-Party Integer Comparison With Block Vectorization Mechanism

2021

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“…Liu et al [138] used the vectorization method to transform the greater-than problem into the computation of the vector. Then, based on the Paillier algorithm, the authors designed a secure protocol that could solve the greater-than problem in one execution.…”

Section: Basic Operationsmentioning

confidence: 99%

A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks

Sun

Zhang

et al. 2020

Sensors

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In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.

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“…However, it demands multiple rounds of computation and communication between the participating parties for sorting multi-party objects based on attribute value. Recently Xin et al also proposed a solution for secure multi-party sorting problem [34]. However, their protocol is based on the assumption that the attributes' value are elements of a universal set, which is known by all participating parties and the computational complexity of the protocol will become high when the size of the universal set is large.…”

Section: Multi-party Secure Computationmentioning

confidence: 99%

Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases

et al. 2019

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Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.

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Efficient Solutions to Two-Party and Multiparty Millionaires’ Problem

Cited by 19 publications

References 32 publications

Efficient Two-Party Integer Comparison With Block Vectorization Mechanism

Efficient Two-Party Integer Comparison With Block Vectorization Mechanism

A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks

Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases

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