A method for privacy-preserving collaborative filtering recommendations

With the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing recommendations of items or users to other users in online environments. Additionally, collaborative filtering methods can be used with a trust network, thus delivering to the user recommendations from both a database of ratings and from users who the person who made the request knows and trusts. On the other hand, the users are having privacy concerns and are not willing to submit the required information (e.g., ratings for products), thus making the recommender system unusable. In this paper, we propose (a) an approach to product recommendation that is based on collaborative filtering and uses a combination of a ratings network with a trust network of the user to provide recommendations and (b) “neighbourhood privacy” that employs a modified privacy-aware role-based access control model that can be applied to databases that utilize recommender systems. Our proposed approach (1) protects user privacy with a small decrease in the accuracy of the recommendations and (2) uses information from the trust network to increase the accuracy of the recommendations, while, (3) providing privacy-preserving recommendations, as accurate as the recommendations provided without the privacy-preserving approach or the method that increased the accuracy applied

Privacy-Preserving Detection Method for Transmission Line Based on Edge Collaboration

Unmanned aerial vehicles (UAVs) are commonly used for edge collaborative computing in current transmission line object detection, where computationally intensive tasks generated by user nodes are offloaded to more powerful edge servers for processing. However, performing edge collaborative processing on transmission line image data may result in serious privacy breaches. To address this issue, we propose a secure single-stage detection model called SecYOLOv7 that preserves the privacy of object detecting. Based on secure multi-party computation (MPC), a series of secure computing protocols are designed for the collaborative execution of Secure Feature Contraction, Secure Bounding-Box Prediction and Secure Object Classification by two non-edge servers. Performance evaluation shows that both computational and communication overhead in this framework as well as calculation error significantly outperform existing works

Data Privacy Preservation in Collaborative Filtering Based Recommender Systems

This dissertation studies data privacy preservation in collaborative filtering based recommender systems and proposes several collaborative filtering models that aim at preserving user privacy from different perspectives. The empirical study on multiple classical recommendation algorithms presents the basic idea of the models and explores their performance on real world datasets. The algorithms that are investigated in this study include a popularity based model, an item similarity based model, a singular value decomposition based model, and a bipartite graph model. Top-N recommendations are evaluated to examine the prediction accuracy. It is apparent that with more customers\u27 preference data, recommender systems can better profile customers\u27 shopping patterns which in turn produces product recommendations with higher accuracy. The precautions should be taken to address the privacy issues that arise during data sharing between two vendors. Study shows that matrix factorization techniques are ideal choices for data privacy preservation by their nature. In this dissertation, singular value decomposition (SVD) and nonnegative matrix factorization (NMF) are adopted as the fundamental techniques for collaborative filtering to make privacy-preserving recommendations. The proposed SVD based model utilizes missing value imputation, randomization technique, and the truncated SVD to perturb the raw rating data. The NMF based models, namely iAux-NMF and iCluster-NMF, take into account the auxiliary information of users and items to help missing value imputation and privacy preservation. Additionally, these models support efficient incremental data update as well. A good number of online vendors allow people to leave their feedback on products. It is considered as users\u27 public preferences. However, due to the connections between users\u27 public and private preferences, if a recommender system fails to distinguish real customers from attackers, the private preferences of real customers can be exposed. This dissertation addresses an attack model in which an attacker holds real customers\u27 partial ratings and tries to obtain their private preferences by cheating recommender systems. To resolve this problem, trustworthiness information is incorporated into NMF based collaborative filtering techniques to detect the attackers and make reasonably different recommendations to the normal users and the attackers. By doing so, users\u27 private preferences can be effectively protected