Private search on key-value stores with hierarchical indexes

Hu, Haibo; Xu, Jianliang; Xu, Xizhong; Pei, Kexin; Choi, Byron; Zhou, Shuigeng

doi:10.1109/icde.2014.6816687

Cited by 17 publications

(3 citation statements)

References 35 publications

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“…Secure protocols against semi-honest adversaries, however, are not only useful in practice but also the foundation of designing secure protocols against malicious adversaries. Therefore, following the the work done in [13], [18], and [24], we also adopt the semi-honest adversaries model in this paper.…”

Section: Approachmentioning

confidence: 99%

Efficient secure similarity computation on encrypted trajectory data

Luo

Zhengy

Liz

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Outsourcing database to clouds is a scalable and cost-effective way for large scale data storage, management, and query processing. Trajectory data contain rich spatiotemporal relationships and reveal many forms of individual sensitive information (e.g., home address, health condition), which necessitate them to be encrypted before being outsourced for privacy concerns. However, efficient query processing over encrypted trajectory data is a very challenging task. Though some achievements have been reported very recently for simple queries (e.g., SQL queries, kNN queries) on encrypted data, there is rather limited progress on secure evaluation of trajectory queries because they are more complex and need special treatment. In this paper, we focus on secure trajectory similarity computation that is the cornerstone of secure trajectory query processing. More specifically, we propose an efficient solution to securely compute the similarity between two encrypted trajectories, which reveals nothing about the trajectories, but the final result. We theoretically prove that our solution is secure against the semihonest adversaries model as all the intermediate information in our protocols can be simulated in polynomial time. Finally we empirically study the efficiency of the proposed method, which demonstrates the feasibility of our solution.978-1-4799-7964-6/15/$31.00

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Section: Approachmentioning

confidence: 99%

Efficient secure similarity computation on encrypted trajectory data

Luo

Zhengy

Liz

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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show abstract

“…During the past few years, for the purpose of handling the aforementioned problem, numerous approaches regarding location privacy protection have been proposed and are gradually showing a personalized trend with the increase of user demands [6]. Although those existing methods have performed very well, there exist some unavoidable limitations.…”

Section: Introductionmentioning

confidence: 99%

A New Distributed User-Demand-Driven Location Privacy Protection Scheme for Mobile Communication Network

Jin¹,

Wu²,

Liu³

2015

International Journal of Distributed Sensor Networks

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With the development of mobile communication networks and intelligent terminals, recent years have witnessed a rapid popularization of location-based service (LBS). While obtaining convenient services, the exploitation of mass location data is inevitably leading to a serious concern about location privacy security. Obviously, high quality of service (QoS) will result in poor location privacy protection, so that a trade-off is needed to fulfill users’ individual demands for both sides. Although existing methods perform well in certain scenarios, few have considered the abovementioned balance problem. Therefore, by combiningk-anonymity-based cloaking technique and obfuscation method, a new distributed user-demand-driven (DUDD) location privacy protection scheme is put forward in this paper. The basic idea is still to select a subcloaking area within the cloaking area generated by Location Anonymization Server. Moreover, by using the improved LBS system model, this paper constructs a distributed framework, in which location privacy protection is wholly occupied in server side and LBS provider is only dedicated to QoS-guarantee. In addition, normalized privacy demand and QoS metrics are given and a user-defined weight parameter is introduced to ensure location privacy security without decreasing QoS. The feasibility of the proposed method is proved through simulation.

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“…On the contrary, protocols secure against the semi-honest attack model are not only applicable in real-world scenarios but also serve as a foundation for designing protocols secure against malicious attack models. Hence, in this paper, we conduct research considering the semi-honest attack model as in previous studies [17,[28][29][30]. Definition 1.…”

mentioning

confidence: 99%

A Parallel Privacy-Preserving k-Means Clustering Algorithm for Encrypted Databases in Cloud Computing

Song,

Kim,

Lee

et al. 2024

Applied Sciences

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With the development of cloud computing, interest in database outsourcing has recently increased. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose decimal-based encryption operation protocols that support privacy preservation. The proposed protocols improve the operational efficiency compared with binary-based encryption operation protocols by eliminating the need for repetitive operations based on bit length. In addition, we propose a privacy-preserving k-means clustering algorithm using decimal-based encryption operation protocols. The proposed k-means clustering algorithm utilizes efficient decimal-based protocols that enhance the efficiency of the encryption operations. To provide high query processing performance, we also propose a parallel k-means clustering algorithm that supports thread-based parallel processing by using a random value pool. Meanwhile, a security analysis of both the proposed k-means clustering algorithm and the proposed parallel algorithm was performed to prove their data protection, query protection, and access pattern protection capabilities. Through our performance analysis, the proposed k-means clustering algorithm shows about 10~13 times better performance compared with the existing algorithms.

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Private search on key-value stores with hierarchical indexes

Cited by 17 publications

References 35 publications

Efficient secure similarity computation on encrypted trajectory data

Efficient secure similarity computation on encrypted trajectory data

A New Distributed User-Demand-Driven Location Privacy Protection Scheme for Mobile Communication Network

A Parallel Privacy-Preserving k-Means Clustering Algorithm for Encrypted Databases in Cloud Computing

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