Significance: This paper belongs to an important class of algorithms that allow one to choose between privacy and accuracy. If data privacy continues to be in the public spotlight, this paper could be a nice addition to that field. Originality: To this reader's knowledge, their approach is novel, borrowing from common techniques in privacy aware learning. Q2: Please summarize your review in 1-2 sentences The paper illustrates a nice application of privacy aware learning to recommendation systems. Further experiments would strengthen the reader's understanding of how the algorithm performs, whether it meets its privacy goals, and how it compares to previous methods.

When you purchase through links in our articles, we may earn a small commission. Windows 11 25H2 is here! With Windows 11 25H2, Microsoft is providing numerous new features for Windows 11. We show you everything you need to know now. Windows 11 version 25H2, the next major feature update for Microsoft's operating system, started rolling out in waves on September 30.

Federated recommendation systems (FedRecs) have gained significant attention for providing privacy-preserving recommendation services. However, existing FedRecs assume that all users have the same requirements for privacy protection, i.e., they do not upload any data to the server. The approaches overlook the potential to enhance the recommendation service by utilizing publicly available user data. In real-world applications, users can choose to be private or public. Private users' interaction data is not shared, while public users' interaction data can be shared. Inspired by the issue, this paper proposes a novel Graph Federated Learning for Personalized Privacy Recommendation (GFed-PP) that adapts to different privacy requirements while improving recommendation performance. GFed-PP incorporates the interaction data of public users to build a user-item interaction graph, which is then used to form a user relationship graph. A lightweight graph convolutional network (GCN) is employed to learn each user's user-specific personalized item embedding. To protect user privacy, each client learns the user embedding and the scoring function locally. Additionally, GFed-PP achieves optimization of the federated recommendation framework through the initialization of item embedding on clients and the aggregation of the user relationship graph on the server. Experimental results demonstrate that GFed-PP significantly outperforms existing methods for five datasets, offering superior recommendation accuracy without compromising privacy. This framework provides a practical solution for accommodating varying privacy preferences in federated recommendation systems.

Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of "public" users who are willing to share their preferences openly, and a large set of "private" users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.

Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of "public" users who are willing to share their preferences openly, and a large set of "private" users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.

In today's multifaceted energy world, a growing number of prosumer assets are increasing the complexity of power grids. This is even more important in an ever-changing climate that more and more generates huge storms such as the Typhoon Lekima which caused 9.3 Billion in damage (5th Costliest known Pacific typhoons) and more than 90 deaths in the Philippines, Taiwan and China earlier this year, or the recent monstrous Category 5 Hurricane Dorian in the Atlantic Ocean. The director-general of the Bahamas Ministry of Tourism and Aviation, Joy Jibrilu, details the damage left in the aftermath from Hurricane Dorian and what the Bahamas will need to move forward especially on the infrastructures. This looks too similar to what we've seen in Porto Rico two years ago which suffered severe damage from the category 5 hurricane Maria. The blackout as a result of Maria has been identified as the largest in US history and the second-largest in world history.

Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of ``public'' users who are willing to share their preferences openly, and a large set of ``private'' users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.