PAPERS: Private and Precise Range Search for Location Based Services

The problem of lack of anonymity and confidentiality can be experienced by those who collect statistical data online as well as those who provide the data. One end may be secure, for example, the one providing data, and yet the other end, for example, the one collecting data, may not be secure. In another scenario, both the data provider and collector may seek anonymity. Preventing the decryption of data provided while providing aggregated results is the best solution for such scenarios. To achieve this, this paper proposes a protocol that puts into application. Homomorphic Encryption and a Distributed Ring algorithm, to ensure data anonymity of both parties involved in a spatial search that is a data provider and a searcher. Firstly, we identify a Homomorphic Encryption technique that can work best for a spatial search by reviewing literature on Homomorphic Encryption techniques. Among the Homomorphic Encryption techniques reviewed were Rivest, Shamir and Adleman (RSA), El Gamal cryptosystem, Goldwasser-Micali cryptosystem, Benaloh cryptosystem, Paillier cryptosystem and Fully Homomorphic Encryption (FHE). After a comprehensive study, Paillier Homomorphic Encryption technique was identified as the best approach to be employed in securing a spatial search. Secondly, we propose a protocol for distributed spatial searching using Paillier cryptosystem and distributed ring algorithm principles. Finally, a proof of concept prototype using the proposed approach was implemented. From initial experiments conducted using the proposed approach, it is evident that the bigger cost comes from the communication over the network and less from the encryption algorithm and protocol itself. A 39.7% overhead when compared to the usefulness of the approach, is outweighed making the solution highly practical and useful.

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“…According to D. Chen et al, Location Based Service (LBS) are becoming more popular on smartphones [34]. One of the common LBS is a range search.…”

Section: Related Workmentioning

confidence: 99%

“…The user can encrypt her location using their method, which uses homomorphic encryption, and the LBS server can calculate distances using the ciphertext. The POIs within the defined range are precisely what LBS returns as results in this manner, and LBS gains no knowledge of the user's actual location [34].…”

Section: Related Workmentioning

confidence: 99%

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

2022

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“…Recently, Homomorphic Encryption for privacy protection is a research hotspot, which can prevent the disclosure of users' information and achieve a more stringent privacy protection. For example, in paper [18], the POI (i.e., the point of interest) could be calculated by LBSP without actually knowing users' locations. The BV Homomorphic Encryption (BVHE) was used in paper [8] to calculate users' request service vector in aggregation process.…”

Section: Technologies Of Lbs Privacy Preservationmentioning

confidence: 99%

An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks

Yang

Zhu

et al. 2019

Applied Sciences

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With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users’ daily lives are facilitated by the applications of LBS, but users’ privacy leaking hinders the further development of LBS. In order to solve this problem, techniques such as k-anonymity and l-diversity have been widely adopted. However, most papers that combine with k-anonymity and l-diversity focus on the security of users’ privacy with little consideration of service efficiency. In this paper, we firstly treat the relationship between k-anonymity and l-diversity in the clustering process from a dynamic and global perspective. Then a service category table based algorithm (SCTB) is designed to identify and calculate l-diversity securely and efficiently, which promotes the cooperative efficiency of users in LBS query, especially when the preference privacy that users request in the clustering process have similarities. Finally, theoretical performance analysis and extensive experimental studies are performed to validate the effectiveness of our SCTB algorithm.

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“…In recent years, domestic and foreign researchers and institutions have widespread attention on location-based privacy preservation; increasingly in-depth studies are carried out in location-based privacy. Besides the above-described location privacy preserving technologies, there are a wealth of methods such as location data randomization [32], fuzzification of space or time data [33,34], methods based on strategies and encryption [35][36][37], and sensitive semantic based security anonymity mechanism [33,[38][39][40].…”

Section: Related Workmentioning

confidence: 99%

RPAR: Location Privacy Preserving via Repartitioning Anonymous Region in Mobile Social Network

Zhang

Yuan

Wang

et al. 2018

Security and Communication Networks

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Applying the proliferated location-based services (LBSs) to social networks has spawned mobile social network (MSN) services that allow users to discover potential friends around them. While enjoying the convenience of MSN services, the mobile users also are confronted with the risk of location disclosure, which is a severe privacy preserving concern. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a repartitioning anonymous region for location privacy preserving (RPAR) scheme based on the central anonymous location which minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location. Furthermore, our scheme enables the users to get more accurate query results, thus improving the quality of the location service. Simulation results show that our proposed scheme can effectively reduce the area of anonymous regions and minimize the traffic.

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PAPERS: Private and Precise Range Search for Location Based Services

Cited by 7 publications

References 9 publications

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks

RPAR: Location Privacy Preserving via Repartitioning Anonymous Region in Mobile Social Network

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