Privacy-Preserving Collaborative Filtering Protocol Based on Similarity between Items

Tada, Minako; Kikuchi, Hiroaki; Puntheeranurak, Sutheera

doi:10.1109/aina.2010.159

Cited by 17 publications

(8 citation statements)

References 10 publications

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“…Berkvosky et al [41] also propose the use of a peer-to-peer decentralised scheme to perform PPCF. In this case, each user has its own rating vector and there is no need for a central server or trust party.…”

Section: B Decentralised Ppcf Methodsmentioning

confidence: 98%

On Privacy Preserving Collaborative Filtering: Current Trends, Open Problems, and New Issues

Casino

Patsakis

Puig

et al. 2013

2013 IEEE 10th International Conference on E-Business Engineering

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Automatic recommender systems have become a cornerstone of e-commerce, especially after the great welcome of Web 2.0 based on participation and interaction of Internet users. Collaborative Filtering (CF) is a recommender system that is becoming increasingly relevant for the industry due to the growth of the Internet, which has made it much more difficult to effectively extract useful information. In this paper, we introduce a taxonomy of the different CF families and we discuss the most relevant Privacy Preserving Collaborative Filtering (PPCF) methods in the literature. To understand the inherent challenges of the PPCF, we also conduct an overview of the current tendencies and major drawbacks of this kind of recommender systems, and we propose several strategies to overcome the shortcomings.

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Section: B Decentralised Ppcf Methodsmentioning

confidence: 98%

On Privacy Preserving Collaborative Filtering: Current Trends, Open Problems, and New Issues

Casino

Patsakis

Puig

et al. 2013

2013 IEEE 10th International Conference on E-Business Engineering

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“…Privacy-preserving collaborative filtering methods can be used to protect user privacy. Different protection methods can be found in the literature (Boutet, A., Frey, D., Guerraoui, R., Jégou, A., & Kermarrec, 2016;Polatidis, Georgiadis, Pimenidis, & Mouratidis, 2017;Songjie Gong, 2011;Tada, Kikuchi, & Puntheeranurak, 2010;Yakut & Polat, 2012). These methods add a level of noise to the data, thus providing a level of protection which comes with a decrease in terms of the quality of the top-N recommendations.…”

Section: How Can User Privacy Be Protected When Collaborative Filterimentioning

confidence: 99%

Mobile recommender systems: Identifying the major concepts

Pimenidis

Polatidis

Mouratidis

2018

Journal of Information Science

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This paper identifies the factors that have an impact on mobile recommender systems. Recommender systems have become a technology that has been widely used by various online applications in situations where there is an information overload problem. Numerous applications such as e-Commerce, video platforms and social networks provide personalized recommendations to their users and this has improved the user experience and vendor revenues. The development of recommender systems has been focused mostly on the proposal of new algorithms that provide more accurate recommendations. However, the use of mobile devices and the rapid growth of the internet and networking infrastructure has brought the necessity of using mobile recommender systems. The links between web and mobile recommender systems are described along with how the recommendations in mobile environments can be improved. This work is focused on identifying the links between web and mobile recommender systems and to provide solid future directions that aim to lead in a more integrated mobile recommendation domain. Keywords Mobile recommender systems, Collaborative filtering, Context, PrivacyRecommender systems, depending on the method they employ can be classified in one of the following categories (Bobadilla et al., 2013; Jannach et al., 2010;Shi, Larson, & Hanjalic, 2014;Su & Khoshgoftaar, 2009):Collaborative filtering. These recommenders suggest items to users that other users with a similar rating history have liked in the past.Content-based. These recommenders suggest items to users that are similar to the items that user has liked in the past.Knowledge-based. These recommenders suggest items to users based either on inferences about the preferences of users or by utilizing specific domain knowledge.

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“…Another method is a homomorphicbased solution by which authors used the Paillier cryptosystem to secure user privacy in user-based CF algorithm [12]. The last one is also based on Paillier cryptosystem by same authors where [12] was improved in-terms of computation and communication costs by introducing privacypreserving item-based CF algorithm [13]. Figure 6 shows the comparison with [12,17] and [13] in-terms of total computation time required to perform average, similarity calculation and recommendation generation (we consider only one type of recommendation; for instance, CF-based recommendation).…”

Section: Computation and Communication Costsmentioning

confidence: 99%

“…The last one is also based on Paillier cryptosystem by same authors where [12] was improved in-terms of computation and communication costs by introducing privacypreserving item-based CF algorithm [13]. Figure 6 shows the comparison with [12,17] and [13] in-terms of total computation time required to perform average, similarity calculation and recommendation generation (we consider only one type of recommendation; for instance, CF-based recommendation). Note that, Kikuchi et al [12] and Tada et al [13] introduced public key-based cryptographic protocol and [17] represented non-cryptographic-based solutions to generate recommendations.…”

Section: Computation and Communication Costsmentioning

confidence: 99%

“…Therefore, they came up with reducing the computation and communication costs by introducing clustering-based CF where users and items are grouped together based on their similarity. Tada et al [13] proved that the cost for generating recommendation can be reduced by using item-based similarity instead of userbased. Therefore, the issue with scalability in the recommender system was handled in their proposed work where privacy was preserved using Paillier cryptosystem.…”

Section: Introductionmentioning

confidence: 99%

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A Practical Privacy-Preserving Recommender System

Badsha

Khalil

2016

Data Sci. Eng.

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The main goal of a personalized recommender system is to provide useful recommendations on various items to the users. In order to generate recommendations, the service needs to access various types of user data such as previous product purchasing history, demographic and biographical information. However, users are sensitive to disclosure of personal information as it can be easily misused by malicious third parties. Consequently, there are unavoidable security concerns which will become known through attempted unauthorized access while providing the recommendation services. In order to protect against breaches of personal information, it is necessary to obfuscate the user information by means of an efficient encryption technique while simultaneously generating the recommendation by making true information inaccessible to the system. To address these challenges, we propose a privacy-preserving recommender system using homomorphic encryption, by which the system can provide recommendations without knowing the actual ratings. Our approach is based on the ElGamal cryptosystem by which both addition and multiplication of plaintexts can be performed. The performance of the proposed scheme shows significantly high accuracy in-terms of computation and communication costs as well as outperforming other existing solutions.

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Privacy-Preserving Collaborative Filtering Protocol Based on Similarity between Items

Cited by 17 publications

References 10 publications

On Privacy Preserving Collaborative Filtering: Current Trends, Open Problems, and New Issues

On Privacy Preserving Collaborative Filtering: Current Trends, Open Problems, and New Issues

Mobile recommender systems: Identifying the major concepts

A Practical Privacy-Preserving Recommender System

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