Efficient Privacy-Enhanced Familiarity-Based Recommender System

Jeckmans, Arjan; Andreas, Peter; Hartel, Pieter H.

doi:10.1007/978-3-642-40203-6_23

Cited by 22 publications

(36 citation statements)

References 22 publications

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“…In this paper, we revisit the concept of friendshipbased recommender system and the protocols from [16], [29]. Generally speaking, we validate the hypothesis made in [16], [29] and comprehensively analyse existing and newly designed protocols.…”

Section: B Our Contributionmentioning

confidence: 63%

“…This is due to the large underlying user populations, which make model training and neighborhood selection unrealistic even with efficient cryptographic tools. Recently, Jeckmans et al proposed an interesting solution direction in [16], where they proposed the concept of friendship-based recommender system and gave solutions based on somewhat homomorphic encryption schemes. The rationale behind their concept is the following.…”

Section: A Related Workmentioning

confidence: 99%

“…Later, Tang and Wang [29] pointed out some security flaws in the protocol from [16] and gave improved solutions. All solutions from [16], [29] rely on the hypothesis that friends share more similar tastes than with strangers without any validation.…”

Section: A Related Workmentioning

confidence: 99%

“…All solutions from [16], [29] rely on the hypothesis that friends share more similar tastes than with strangers without any validation. Moreover, some proper analysis is missing for security and implementation results are missing to demonstrate the efficiency.…”

Section: A Related Workmentioning

confidence: 99%

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Privacy-Preserving Friendship-Based Recommender Systems

Tang

Wang

2018

IEEE Trans. Dependable and Secure Comput.

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Privacy-preserving recommender systems have been an active research topic for many years. However, until today, it is still a challenge to design an efficient solution without involving a fully trusted third party or multiple semitrusted third parties. The key obstacle is the large underlying user populations (i.e. huge input size) in the systems. In this paper, we revisit the concept of friendship-based recommender systems, proposed by Jeckmans et al. and Tang and Wang. These solutions are very promising because recommendations are computed based on inputs from a very small subset of the overall user population (precisely, a user's friends and some randomly chosen strangers). We first clarify the single prediction protocol and Top-n protocol by Tang and Wang, by correcting some flaws and improving the efficiency of the single prediction protocol. We then design a decentralized single protocol by getting rid of the semi-honest service provider. In order to validate the designed protocols, we crawl Twitter and construct two datasets (FMT and 10-FMT) which are equipped with auxiliary friendship information. Based on 10-FMT and MovieLens 100k dataset with simulated friendships, we show that even if our protocols use a very small subset of the datasets, their accuracy can still be equal to or better than some baseline algorithm. Based on these datasets, we further demonstrate that the outputs of our protocols leak very small amount of information of the inputs, and the leakage decreases when the input size increases. We finally show that he single prediction protocol is quite efficient but the Top-n is not. However, we observe that the efficiency of the Top-n protocol can be dramatically improved if we slightly relax the desired security guarantee.

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Section: B Our Contributionmentioning

confidence: 63%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

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Privacy-Preserving Friendship-Based Recommender Systems

Tang

Wang

2018

IEEE Trans. Dependable and Secure Comput.

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“…For example, two illustrative works [23,24] propose the use of homomorphic encryption as a structural element for their systems. Another system that uses randomized perturbation instead of encryption has been proposed in [25].…”

Section: Privacy-enhanced Recommender Systemsmentioning

confidence: 99%

A Privacy-by-Design Contextual Suggestion System for Tourism

Efraimidis

Drosatos

Arampatzis

et al. 2016

JSAN

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Abstract:We focus on personal data generated by the sensors and through the everyday usage of smart devices and take advantage of these data to build a non-invasive contextual suggestion system for tourism. The system, which we call Pythia, exploits the computational capabilities of modern smart devices to offer high quality personalized POI (point of interest) recommendations. To protect user privacy, we apply a privacy by design approach within all of the steps of creating Pythia. The outcome is a system that comprises important architectural and operational innovations. The system is designed to process sensitive personal data, such as location traces, browsing history and web searches (query logs), to automatically infer user preferences and build corresponding POI-based user profiles. These profiles are then used by a contextual suggestion engine to anticipate user choices and make POI recommendations for tourists. Privacy leaks are minimized by implementing an important part of the system functionality at the user side, either as a mobile app or as a client-side web application, and by taking additional precautions, like data generalization, wherever necessary. As a proof of concept, we present a prototype that implements the aforementioned mechanisms on the Android platform accompanied with certain web applications. Even though the current prototype focuses only on location data, the results from the evaluation of the contextual suggestion algorithms and the user experience feedback from volunteers who used the prototype are very positive.

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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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Efficient Privacy-Enhanced Familiarity-Based Recommender System

Cited by 22 publications

References 22 publications

Privacy-Preserving Friendship-Based Recommender Systems

Privacy-Preserving Friendship-Based Recommender Systems

A Privacy-by-Design Contextual Suggestion System for Tourism

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

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