Cryptographically-enhanced privacy for recommender systems

Jeckmans, Adrianus Johannus Paulus

doi:10.3990/1.9789036535939

Cited by 6 publications

(5 citation statements)

References 97 publications

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“…80 percent data is used for training and 20 percent is used for testing. The computed RMSE score for RDT-P, Homo-S, and Homo-3bit datasets along with few existing schemes [42]- [45] are shown in Figure 8. The Homomorphic encryption [42] and k-anonymized rating [43] schemes perform almost similar to original dataset where random perturbation [45] and multi-level [44] perform worst but better than Homo-3bit scheme.…”

Section: E Rdt Application In Recommendation Domainmentioning

confidence: 99%

“…The computed RMSE score for RDT-P, Homo-S, and Homo-3bit datasets along with few existing schemes [42]- [45] are shown in Figure 8. The Homomorphic encryption [42] and k-anonymized rating [43] schemes perform almost similar to original dataset where random perturbation [45] and multi-level [44] perform worst but better than Homo-3bit scheme. The RDT-P perform better in terms of RMSE even from the original dataset because it create dataset in the range of 1 − 3 with low variance.…”

Section: E Rdt Application In Recommendation Domainmentioning

confidence: 99%

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Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

et al. 2021

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The protection and processing of the sensitive data in recommendation system are the major concern. Existing literature, used homomorphic encryption (HE), Reversible Data Transform (RDT), differential privacy (DP) and many more schemes to protect user information. Existing RDT scheme require prior sharing of the parameters and an alternative mechanism e.g., Shamir Threshold Protocol or Diffiehellman algorithm are used to protect the sharing parameters. In this paper, we proposed a chaotic based RDT approach for privacy-preserving data mining (PPDM) in recommendation system. Using this approach, RDT parameter values will be generated locally and because of this, prior sharing of the parameter values for the recovery process will not be necessary. This approach can be used as an alternative to the standard-RDT algorithm where bandwidth and memory are considered important factors. Our results on the Iris data set clearly show that the proposed chaotic RDT shows similar results as standard-RDT. Secondly, in this paper, we explore the usage of the RDT algorithm on real app usage records in the mobile app recommendation (MAR) domain. Thirdly, we tested the application of the RDT algorithm for the standard MovieLens dataset to ensure the validity of results because app usage dataset is publicly not available. Our results show that the proposed RDT algorithm can replace HE if an adaptive recommendation approach is used. Similarly, we can safely use the RDT approach to any data including user rating, health data or app usage frequency to ensure user privacy before delivering it to the recommender-server.INDEX TERMS Movie recommendation system (RS); reversible Data transform (RDT); reversible integer transform (RIT); privacy-preservation data mining (PPDM); App recommendation system (MARS); homomorphic encryption (HE).

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Section: E Rdt Application In Recommendation Domainmentioning

confidence: 99%

Section: E Rdt Application In Recommendation Domainmentioning

confidence: 99%

Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

et al. 2021

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“…In Refs , the authors survey and review CF and PPCF schemes. Practical issues in different partitioning cases and various partitioning scenarios are discussed in Refs . Ref introduces the CF concept, while Ref proposes a new measure for privacy evaluation.…”

Section: Dimensions For Classifying Ppcf Schemesmentioning

confidence: 99%

“…Thus, the method is able to efficiently provide recommendations. Ref investigates how to use secure computation in such a way that privacy is achieved while making the computations as efficient as possible. To achieve this goal, the author develops specialized secure computation protocols based on secure multiparty computation and HE.…”

Section: Existing Trends In Ppcfmentioning

confidence: 99%

From existing trends to future trends in privacy‐preserving collaborative filtering

Ozturk

Polat

2015

WIREs Data Min & Knowl

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The information overload problem, also known as infobesity, forces online vendors to utilize collaborative filtering algorithms. Although various recommendation methods are widely used by many electronic commerce sites, they still have substantial problems, including but not limited to privacy, accuracy, online performance, scalability, cold start, coverage, grey sheep, robustness, being subject to shilling attacks, diversity, data sparsity, and synonymy. Privacy-preserving collaborative filtering methods have been proposed to handle the privacy problem. Due to the increasing popularity of privacy protection and recommendation estimation over the Internet, prediction schemes with privacy are still receiving increasing attention. Because research trends might change over time, it is critical for researchers to observe future trends. In this study, we determine the existing trends in the privacypreserving collaborative filtering field by examining the related papers published mainly in the last few years. Comprehensive examinations of the most up-to-date related studies are described. By scrutinizing the contemporary inclinations, we present the most promising possible research trends in the near future. Our proposals can help interested researchers direct their research toward better outcomes and might open new ways to enrich privacy-preserving collaborative filtering studies.

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“…However, achieving such goal is not always possible for some e-companies, especially the newly established ones, due to lack of necessary and sufficient data, and it turns out to be a challenge [8], [9]. Companies overcome such shortage by collaborating with other companies with the aim of providing more meaningful recommendations based on the aggregated data [10]. However, there are risks and obstacles in establishing such cooperation.…”

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confidence: 99%

Privacy-Aware Detection of Shilling Profiles on Arbitrarily Distributed Recommender Systems

2019

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Due to the mutual advantage of small-scale online service providers, they need to collaborate to deliver recommendations based on arbitrarily distributed preference data without jeopardizing their confidentiality. Besides privacy issues, parties also have concerns regarding the vulnerability against recommendation manipulation attempts, referred to as shilling attacks. Although there are methods for detecting these injected malicious profiles in central server-based configurations, they are not readily suitable for employing arbitrarily distributed data. In this paper, we present a novel classification-based shilling attack detection protocol enabling the recognition of malicious profiles in arbitrarily distributed configurations without compromising the privacy of collaborating parties. The analysis of the proposed protocol regarding confidentiality of parties reveals that the process is bound to collaboration by design, which does not allow parties to achieve detection by themselves. Furthermore, empirical evaluations using real-world preference data demonstrate that the protocol can achieve significantly high detection rates facilitating privacy-aware data collaboration.

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Cryptographically-enhanced privacy for recommender systems

Cited by 6 publications

References 97 publications

Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

From existing trends to future trends in privacy‐preserving collaborative filtering

Privacy-Aware Detection of Shilling Profiles on Arbitrarily Distributed Recommender Systems

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