APPLET: a privacy-preserving framework for location-aware recommender system

Ma, Xindi; Li, Hui; Ma, Jianfeng; Jiang, Qi; Gao, Sheng; Xi, Ning; Lu, Di

doi:10.1007/s11432-015-0981-4

Cited by 34 publications

(6 citation statements)

References 19 publications

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“…As to the Adult Dataset, for each computing nodes, we generate 300K objects based on its 34K data. The number of object attributes is in the range of [2,5]; meanwhile, the values of the object attributes are in the range of [0, 10 4 ]. We divide the data into N * cells of the same size, where N * is an integral multiple of N. Each cell and the corresponding data are randomly transmitted to the computing nodes.…”

Section: Performance Analysismentioning

confidence: 99%

“…As a successful case deployed on the EC 2 , the Nimbus Health , manages and distributes patient medical records by the cloud service; in addition, the ShareThis , treated as a content‐sharing social network, has shared 430 million items across 30 000 websites. Since data in cloud servers is shared to all query users, if without any privacy‐preserving procedures, sensitive information of data publishers may be revealed to malicious users 2 . If the privacy information of cloud users cannot be protected necessarily, the future development of public cloud will be extremely worrying.…”

Section: Introductionmentioning

confidence: 99%

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A multi‐dimensional index for privacy‐preserving queries in cloud computing

Ding

Jin

2019

Concurrency and Computation

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Although cloud computing saves the cost of enterprises and individuals to manage data in various applications on public cloud servers, it also causes the problem of the leakage of critical personal information and sensitive data. Therefore, the issues about data privacy of the published personal information have become a challenging problem. Most of the existing methods focus on cryptography-based techniques by encrypting the sensitive data before publishing. However, these techniques are either too computation expensive or only some authorized users can get access to the encrypted cloud data. The consequence is that the search response time is not satisfactory and the sharing extent of the cloud data is becoming limited. Motivated by this, we propose a perturbation-based method to preserve the privacy of data in cloud. To accelerate the query processing without privacy breaches, a high-efficiency multi-dimensional index is built for answering range counting queries under differential privacy, which embeds privacy-preserving R-tree index in Content Addressable Network. Experimental results demonstrate that our methods not only protect data privacy in different degree, but also accelerate the query speed efficiently. Compared with the existing method Quad-opt, our method provides about 20% better data utility under the same differential privacy principles.

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Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi‐dimensional index for privacy‐preserving queries in cloud computing

Ding

Jin

2019

Concurrency and Computation

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“…To enable recommender systems to carry out computation over ciphertext directly, the encryption algorithms to be used have to be homomorphic, that is, the result of operations performed on ciphertext, when decrypted, matches the result of operations performed on the corresponding plaintext. For example, Paillier cryptosystem [14] was employed by Erkin et al [6] and Ma et al [10], ElGamal cryptosystem [5] was used by Zhan et al [17] and Badsha et al [1], to realize privacy-preserving recommender systems. However, homomorphic encryption is built on expensive public-key cryptography, which is theoretical in nature and cannot be applied in practice due to the prohibitive computation cost.…”

Section: Related Workmentioning

confidence: 99%

When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System

Liu

Luo

Zhang

et al. 2017

Lecture Notes in Computer Science

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Privacy risks of recommender systems have caused increasing attention. Users' private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users' private data. Existing approaches focus either on keeping users' private data protected during recommendation computation or on preventing the inference of any single user's data from the recommendation result. However, none is designed for both hiding users' private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.

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“…On the other hand, location trajectories can excavate more sensitive information despite location. In this regard, location privacy-preserving mechanisms have been extensively studied, such as Dummy, spatial confusion [3,4], encryption technology [5,6], differential privacy (DP) [7,8] and references therein.…”

mentioning

confidence: 99%