Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things

Yin, Chunyong; Xi, Jinwen; Sun, Ruizhi; Wang, Jin

doi:10.1109/tii.2017.2773646

Cited by 268 publications

(119 citation statements)

References 17 publications

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“…Wu et al [20] introduced a continuous location entropy and trajectory entropy based on the gravity mobility model in the Internet of Things. Yin et al [21] proposed a location privacy protection scheme that satisfies the differential privacy constraint to protect the user's location information privacy, maximizing data availability in the industrial Internet of Things. Wu et al [22] introduced a privacy protection scheme to solve the privacy protection problem in the Internet of Things, named EUROCRYPT15.…”

Section: Privacy Protection and Security In The Internet Of Thingsmentioning

confidence: 99%

“…The reason is that the Enhanced-DLP scheme can guarantee that the chosen dummy locations comprise an anonymous set with the largest entropy and enough dummy locations whose query probability are the same as the user's real location, thus the LBS server still cannot identify the user's real location from k locations. From Step (21) to Step (29), as the chosen k dummy locations have a similar historical query probability and the Enhanced-DLP ensures the uncertainty of the selection, the LBS server cannot identify the user's real location from k locations.…”

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confidence: 99%

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An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

Cai

Zhang

et al. 2019

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With the rapid development of GPS-equipped smart mobile devices and mobile computing, location-based services (LBS) are increasing in popularity in the Internet of Things (IoT). Although LBS provide enormous benefits to users, they inevitably introduce some significant privacy concerns. To protect user privacy, a variety of location privacy-preserving schemes have been recently proposed. Among these schemes, the dummy-based location privacy-preserving (DLP) scheme is a widely used approach to achieve location privacy for mobile users. However, the computation cost of the existing dummy-based location privacy-preserving schemes is too high to meet the practical requirements of resource-constrained IoT devices. Moreover, the DLP scheme is inadequate to resist against an adversary with side information. Thus, how to effectively select a dummy location is still a challenge. In this paper, we propose a novel lightweight dummy-based location privacy-preserving scheme, named the enhanced dummy-based location privacy-preserving(Enhanced-DLP) to address this challenge by considering both computational costs and side information. Specifically, the Enhanced-DLP adopts an improved greedy scheme to efficiently select dummy locations to form a k-anonymous set. A thorough security analysis demonstrated that our proposed Enhanced-DLP can protect user privacy against attacks. We performed a series of experiments to verify the effectiveness of our Enhanced-DLP. Compared with the existing scheme, the Enhanced-DLP can obtain lower computational costs for the selection of a dummy location and it can resist side information attacks. The experimental results illustrate that the Enhanced-DLP scheme can effectively be applied to protect the user’s location privacy in IoT applications and services.

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Section: Privacy Protection and Security In The Internet Of Thingsmentioning

confidence: 99%

mentioning

confidence: 99%

An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

Cai

Zhang

et al. 2019

Information

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“…It is mainly aimed at the query and release of location information. Related location privacy protection technologies include location anonymous technologies, among which application research of kanonymous technology proposed by Samarati and Sweeney is the most widely used [7]: encryption technologies, such as secure multiparty computing technology, homomorphic encryption technology, fake convergence node protocol, and RFID privacy protection technology [8]. IoT location privacy protection technology has gradually matured, but protection technology related to mobile location privacy is still in its infancy.…”

Section: Research Statusmentioning

confidence: 99%

The Research of Mobile Location Privacy Protection Access Control Method Based on Game Theory

Zheng

Zhang

Cui

et al. 2018

Wireless Communications and Mobile Computing

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In recent years, the Internet of things has developed rapidly. And the location-based service (LBS) is becoming more and more extensive. Service providers hold a large number of users’ information. In order to improve the quality of service, service providers increasingly use big data technology to provide more accurate services for users. At the same time, it aggravates the information disclosure of users’ privacy. From the perspective of the service provider, a mobile location privacy access control method based on game theory is proposed to solve the access control problem of mobile location privacy information. Firstly, the weight coefficient is set according to the location privacy influence factors, and then the access control threshold is calculated according to the privacy location leakage situation of the mobile location. Different visitor levels are set according to the threshold. In the process of access control, the prejudgement of the access behaviour is performed, and then the privacy information amount of the information requested for access is calculated according to the weights of the different information. Compare the result with the threshold and get the access control strategy. The strategy set is selected based on strategy matrix of game theory and thresholds are adjusted based on the calculation returns of strategy matrix. The effectiveness and practicability of the method are verified through the security analysis.

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“…In recent years, various technologies have been broadly used for data privacy protection, such as private information retrieval [6][7][8][9][10][11], searchable encryption [12][13][14][15][16][17], and secure multi-party computation [18][19][20][21][22][23], but these technologies can only provide limited functions, such as keyword search, order search, range query, and subset search. However, for many application scenarios in the cloud environment, it requires various types operations of ciphertext data.…”

Section: Introductionmentioning

confidence: 99%

A privacy protection-oriented parallel fully homomorphic encryption algorithm in cyber physical systems

Min

Yang

Sangaiah

et al. 2019

J Wireless Com Network

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Cyber physical system (CPS) is facing enormous security challenges because of open and interconnected network and the interaction between cyber components and physical components, the development of cyber physical systems is constrained by security and privacy threats. A feasible solution is to combine the fully homomorphic encryption (FHE) technique to realize the efficient operation of ciphertext without decryption. However, most current homomorphic encryption algorithms only support limited data types, making it difficult to be widely applied in actual environment. To address this limitation, we propose a parallel fully homomorphic encryption algorithm that supports floating-point numbers. The proposed algorithm not only expands the data types supported by the existing fully homomorphic encryption algorithms, but also utilizes the characteristics of multi-nodes in cloud environment to conduct parallel encryption through simultaneous group-wise ciphertext computations. The experimental results show that, in a 16-core 4-node cluster with MapReduce environment, the proposed encryption algorithm achieves the maximum speed-up exceeding 5, which not only solves the limited application problem of the existing fully homomorphic encryption algorithm, but also meets the requirements for the efficient homomorphic encryption of floating-point numbers in cloud computing environment.

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Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things

Cited by 268 publications

References 17 publications

An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services

The Research of Mobile Location Privacy Protection Access Control Method Based on Game Theory

A privacy protection-oriented parallel fully homomorphic encryption algorithm in cyber physical systems

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