A privacy-protecting architecture for collaborative filtering via forgery and suppression of ratings

Recommendation systems are information-filtering systems that help users deal with information overload. Unfortunately, current recommendation systems prompt serious privacy concerns. In this work, we propose an architecture that protects user privacy in such collaborative-filtering systems, in which users are profiled on the basis of their ratings. Our approach capitalizes on the combination of two perturbative techniques, namely the forgery and the suppression of ratings. In our scenario, users rate those items they have an opinion on. However, in order to avoid privacy risks, they may want to refrain from rating some of those items, and/or rate some items that do not reflect their actual preferences. On the other hand, forgery and suppression may degrade the quality of the recommendation system. Motivated by this, we describe the implementation details of the proposed architecture and present a formulation of the optimal trade-off among privacy, forgery rate and suppression rate. Finally, we provide a numerical example that illustrates our formulation.Peer ReviewedPostprint (published version

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Data Service Outsourcing and Privacy Protection in Mobile Internet

Mobile Internet data have the characteristics of large scale, variety of patterns, and complex association. On the one hand, it needs efficient data processing model to provide support for data services, and on the other hand, it needs certain computing resources to provide data security services. Due to the limited resources of mobile terminals, it is impossible to complete large-scale data computation and storage. However, outsourcing to third parties may cause some risks in user privacy protection. This monography focuses on key technologies of data service outsourcing and privacy protection, including the existing methods of data analysis and processing, the fine-grained data access control through effective user privacy protection mechanism, and the data sharing in the mobile Internet