Location privacy-preserving k nearest neighbor query under user’s preference

Ni, Weiwei; Gu, Mingzhu; Chen, Xiao

doi:10.1016/j.knosys.2016.03.016

Cited by 42 publications

(24 citation statements)

References 7 publications

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“…In Fig. 7, if the LBS user who is located in the cell that is identified by row number five and column number three (i.e., the coordinates C [5,3]) wants to protect his/her location privacy by achieving 4-anonymity level (i.e., k=4), he/she can select three of the cells that are marked by the √ symbol. Since the query probability of any of the three selected cells equals the query probability of the original cell, the attacker cannot determine the real location of the LBS user among the k-1 dummy locations.…”

Section: ) Wise Dummy Selection Location (Wdsl) Approachmentioning

confidence: 99%

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Agent-Based System for Efficient kNN Query Processing with Comprehensive Privacy Protection

Alrahhal¹,

Khemakhem²,

Jambi³

2018

ijacsa

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Abstract-Recently, location based services (LBSs) have become increasingly popular due to advances in mobile devices and their positioning capabilities. In an LBS, the user sends a range of queries regarding his k-nearest neighbors (kNNs) that have common points of interests (POIs) based on his real geographic location. During the query sending, processing, and responding phases, private information may be collected by an attacker, either by tracking the real locations or by analyzing the sent queries. This compromises the privacy of the user and risks his/her safety in certain cases. Thus, the objective of this paper is to ensure comprehensive privacy protection, while also guaranteeing the efficiency of kNN query processing. Therefore, we propose an agent-based system for dealing with these issues. The system is managed by three software agents ( , , and ). The agent executes a Wise Dummy Selection Location (WDSL) algorithm to ensure the location privacy. The mission of the agent is integrated with the mission of the agent, which is to ensure the query privacy based on Left-Right Fragmentation (LRF) algorithm. To guarantee the efficiency of kNN processing, the agent executes a prediction phase depending on a Cell Based Indexing (CBI) technique. Compared to similar privacy protection approaches, the proposed WDSL and LRF approaches showed higher resistance against location homogeneity attacks and query sampling attacks. In addition, the proposed CBI indexing technique obtains more accurate answers to kNN queries than the previous indexing techniques.

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Section: ) Wise Dummy Selection Location (Wdsl) Approachmentioning

confidence: 99%

“…2) Danger of location homogeneity inference attack If the LBS user selects cells C [5,1], C [4,2], and C [6,3], as shown in Fig. 8, some personal information can be inferred by the attacker without the need to determine the real location of the LBS user.…”

Section: ) Wise Dummy Selection Location (Wdsl) Approachmentioning

confidence: 99%

Agent-Based System for Efficient kNN Query Processing with Comprehensive Privacy Protection

Alrahhal¹,

Khemakhem²,

Jambi³

2018

ijacsa

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“…As an essential data‐mining operation, the kNN query can be used as a vital component of many data mining approaches 7 . In the past few years, to support the kNN query over encrypted cloud databases, many types of research have been proposed 7‐19 . Those works usually involve three parties: the cloud server (CS), the database owner (DO), and the query user (QU).…”

Section: Introductionmentioning

confidence: 99%

Lightweight edge‐based kNN privacy‐preserving classification scheme in cloud computing circumstance

Tan

Liu

et al. 2020

Concurrency and Computation

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Summary Because mobile terminals have limited computing and storage resources, individuals tend to outsource their data generated from mobile devices to clouds to do data operations. However, utilization of the abundant computation and storage resources of clouds may pose a threat to user's private data. In this paper, we focus on the issue of encrypted k‐nearest neighbor (kNN) classification on the cloud. In the past few years, many solutions were proposed to protect the user's privacy and data security. Unfortunately, most privacy‐preserving data mining schemes are not lightweight, which are not practical in real‐world applications. To solve this issue, we proposed a lightweight edge‐based kNN (EBkNN) classification scheme over encrypted cloud database utilizing edge computing technology. Our proposed scheme can provide several security guarantees: (i) user's data security, (ii) user's query privacy, and (iii) data access patterns. We analyzed the security of our scheme utilizing the semi‐honest security model and evaluated the performance using a synthetic dataset. The experiment results indicate that our scheme is more lightweight than the state‐of‐the‐art scheme.

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“…In order to overcome the shortcomings of the centralized structure, more and more researches turn to the models with the distributed structure. For example, [10][11][12][13][14] all adopted the distributed architecture. The distributed structure system is only composed of the mobile terminals and SP.…”

Section: Introductionmentioning

confidence: 99%

A New Group Location Privacy-Preserving Method Based on Distributed Architecture in LBS

Wang

Han

Yang

et al. 2019

Security and Communication Networks

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Nowadays, the location privacy problem has become an important problem for the users who enjoy the location-based services (LBSs). Researchers have focused on the problem of how to protect the location privacy of user efficiently for a long time. On one hand, many achievements adopt the centralized structure in which there is an additional center server. Additionally, some other researchers adopt the distributed structure to overcome the disadvantages brought by the center server in the centralized anonymous system structure. On the other hand, the existing methods of solving the problem are always to protect the individual user’s location privacy in LBSs, without considering the user group’s location privacy. This kind of methods is not very applicable to the status of a number of users who formed a group to complete a LBS task together by collaborative computing. In order to solve the problem of location privacy protection for a user group in the untrusted mobile social networks, a location privacy protection method based on the distributed structure is discussed in this paper. In the scheme, the special homomorphic features of BGN cryptosystem are cleverly used so that it can solve the group’s three classical location service applications simultaneously, namely, group nearest neighbor query, optimal group collection point determination, and group friend’s distance query, by only one security policy. If there are k users who formed the group, it could achieve k-anonymity without exposing the coordinate of each individual user or using any anonymous areas. Furthermore, theoretical and experimental analysis proves that the proposal can efficiently protect each user’s location privacy in the group through taking full advantage of the collaborative computing and communication capabilities of the mobile terminals. It can resist the existing distance interaction attack and collusion attack and can realize the secure and efficient fine-grained controllable location privacy protection for the user group.

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Location privacy-preserving k nearest neighbor query under user’s preference

Cited by 42 publications

References 7 publications

Agent-Based System for Efficient kNN Query Processing with Comprehensive Privacy Protection

Agent-Based System for Efficient kNN Query Processing with Comprehensive Privacy Protection

Lightweight edge‐based kNN privacy‐preserving classification scheme in cloud computing circumstance

A New Group Location Privacy-Preserving Method Based on Distributed Architecture in LBS

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