Privacy preserving cooperative computation for personalized web search applications

Kaaniche, Nesrine; Masmoudi, Souha; Znina, Souha; Laurent, Maryline; Demir, Levent

doi:10.1145/3341105.3373947

Cited by 6 publications

(4 citation statements)

References 17 publications

(20 reference statements)

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“…Therefore, based on the above considerations, Kaaniche et al [26] designed a privacy protection framework based on user preference and proposed a secure computation based on collaboration to interfere with the configuration file. The computation includes intermediate nodes between the client and the service provider, but this method will generate more extra computation overhead.…”

Section: Related Workmentioning

confidence: 99%

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Zhang

Song

2023

IJGI

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In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser‘ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network.

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Section: Related Workmentioning

confidence: 99%

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Zhang

Song

2023

IJGI

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“…However, the same random grouping is used in MG-OSLo, and the chance of being grouped with users having similar interests remains the same. Kaaniche et al proposed a decentralized solution CoWSA that empowers end-users to have control over personal data, mitigates single-point failure, ensures the security of the queries, and provides anonymity to a user [1]. User, client, WSE, third parties (TP), and trusted authorities are the five entities of CoWSA.…”

Section: Existing Workmentioning

confidence: 99%

“…e WSE stores the users' submitted queries in a query log. e WSE regularly builds and updates a user profile from the query log to provide personalized results [1]. e WSE generates revenue by analyzing the query log coupled with a user profile to provide relevant advertisements [2,3].…”

Section: Introductionmentioning

confidence: 99%

Profile Aware ObScure Logging (PaOSLo): A Web Search Privacy-Preserving Protocol to Mitigate Digital Traces

Ullah

Khan

et al. 2022

Security and Communication Networks

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Web search querying is an inevitable activity of any Internet user. The web search engine (WSE) is the easiest way to search and retrieve data from the Internet. The WSE stores the user’s search queries to retrieve the personalized search result in a form of query log. A user often leaves digital traces and sensitive information in the query log. WSE is known to sell the query log to a third party to generate revenue. However, the release of the query log can compromise the security and privacy of a user. In this work, we propose a Profile Aware ObScure Logging (PaOSLo) Web search privacy-preserving protocol that mitigates the digital traces a user leaves in Web searching. PaOSLo systematically groups users based on profile similarity. The primary objective of this work is to evaluate the impact of the systematic group compared to random grouping. We first computed the similarity between the users’ profiles and then clustered them using the K-mean algorithm to group the users systematically. Unlikability and indistinguishability are the two dimensions in which we have measured the privacy of a user. To compute the impact of systematic grouping on a user’s privacy, we have experimented with and compared the performance of PaOSLo with modern distributed protocols like OSLo and UUP(e). Results show that, at the top degree of the ODP hierarchy, PaOSLo preserved 10% and 3% better profile privacy than the modern distributed protocols mentioned above. In addition, the PaOSLo has less profile exposure for any group size and at each degree of the ODP hierarchy.

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“…Two main axes based on cryptography and data perturbation are investigated. Kaaniche et al [11] designed a privacy-preserving framework for recommender systems. They suggested that a user perturbs his profile relying on a collaborative secure computation, that incorporates intermediate nodes between end-users and service providers.…”

Section: Related Workmentioning

confidence: 99%

A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy

Rahali¹,

Laurent²,

Masmoudi³

et al. 2021

Preprint

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This paper proposes a new recommendation system preserving both privacy and utility. It relies on the local differential privacy (LDP) for the browsing user to transmit his noisy preference profile, as perturbed Bloom filters, to the service provider.The originality of the approach is multifold. First, as far as we know, the approach is the first one including at the user side two perturbation rounds -PRR (Permanent Randomized Response) and IRR (Instantaneous Randomized Response) -over a complete user profile. Second, a full validation experimentation chain is set up, with a machine learning decoding algorithm based on neural network or XGBoost for decoding the perturbed Bloom filters and the clustering Kmeans tool for clustering users. Third, extensive experiments show that our method achieves good utility-privacy trade-off, i.e. a 90% clustering success rate, resp. 80.3% for a value of LDP = 0.8, resp. = 2. Fourth, an experimental and theoretical analysis gives concrete results on the resistance of our approach to the plausible deniability and resistance against averaging attacks.

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Privacy preserving cooperative computation for personalized web search applications

Cited by 6 publications

References 17 publications

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Profile Aware ObScure Logging (PaOSLo): A Web Search Privacy-Preserving Protocol to Mitigate Digital Traces

A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy

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