User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Towards privacy-preserving and fairness-enhanced item ranking in recommender systems

Nous présentons une nouvelle approche de préservation de la vie privée pour améliorer l’équité des éléments dans les systèmes de classement. Nous utilisons des techniques de post-traitement dans un environnement de recommandation multipartite afin d’équilibrer l’équité et la protection de la vie privée pour les producteurs et les consommateurs. Notre méthode utilise des serveurs de calcul multipartite sécurisés (MPC) et une confidentialité différentielle (DP) pour maintenir la confidentialité des utilisateurs tout en atténuant l’injustice des éléments sans compromettre l’utilité. Les utilisateurs soumettent leurs données sous forme de partages secrets aux serveurs MPC, et tous les calculs sur ces données restent cryptés. Nous évaluons notre approche à l’aide d’ensembles de données du monde réel, tels qu’Amazon Digital Music, Book Crossing et MovieLens-1M, et analysons les compromis entre confidentialité, équité et utilité. Notre travail encourage une exploration plus approfondie de l’intersection de la confidentialité et de l’équité dans les systèmes de recommandation, jetant les bases de l’intégration d’autres techniques d’amélioration de la confidentialité afin d’optimiser l’exécution et l’évolutivité pour les applications du monde réel. Nous envisageons notre approche comme un tremplin vers des solutions de bout en bout préservant la confidentialité et promouvant l’équité dans des environnements de recommandation multipartites.We present a novel privacy-preserving approach to enhance item fairness in ranking systems. We employ post-processing techniques in a multi-stakeholder recommendation environment in order to balance fairness and privacy protection for both producers and consumers. Our method utilizes secure multi-party computation (MPC) servers and differential privacy (DP) to maintain user privacy while mitigating item unfairness without compromising utility. Users submit their data as secret shares to MPC servers, and all calculations on this data remain encrypted. We evaluate our approach using real-world datasets, such as Amazon Digital Music, Book Crossing, and MovieLens-1M, and analyze the trade-offs between privacy, fairness, and utility. Our work encourages further exploration of the intersection of privacy and fairness in recommender systems, laying the groundwork for integrating other privacy-enhancing techniques to optimize runtime and scalability for real-world applications. We envision our approach as a stepping stone towards end-to-end privacy-preserving and fairness-promoting solutions in multi-stakeholder recommendation environments

Privacy-preserving collaboration in an integrated social environment

Privacy and security of data have been a critical concern at the state, organization and individual levels since times immemorial. New and innovative methods for data storage, retrieval and analysis have given rise to greater challenges on these fronts. Online social networks (OSNs) are at the forefront of individual privacy concerns due to their ubiquity, popularity and possession of a large collection of users' personal data. These OSNs use recommender systems along with their integration partners (IPs) for offering an enriching user experience and growth. However, the recommender systems provided by these OSNs inadvertently leak private user information. In this work, we develop solutions targeted at addressing existing, real-world privacy issues for recommender systems that are deployed across multiple OSNs. Specifically, we identify the various ways through which privacy leaks can occur in a friend recommendation system (FRS), and propose a comprehensive solution that integrates both Differential Privacy and Secure Multi-Party Computation (MPC) to provide a holistic privacy guarantee. We model a privacy-preserving similarity computation framework and library named Lucene-P2. It includes the efficient privacy-preserving Latent Semantic Indexing (LSI) extension. OSNs can use the Lucene-P2 framework to evaluate similarity scores for their private inputs without sharing them. Security proofs are provided under semi-honest and malicious adversary models. We analyze the computation and communication complexities of the protocols proposed and empirically test them on real-world datasets. These solutions provide functional efficiency and data utility for practical applications to an extent.Includes bibliographical references