Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data.
This paper proposes a system, entitled Concealer that allows sharing time-varying spatial data (e. g., as produced by sensors) in encrypted form to an untrusted third-party service provider to provide location-based applications (involving aggregation queries over selected regions over time windows) to users.
In this paper, we introduce Quest, a system that empowers organizations to observe individuals and spaces to implement policies for social distancing and contact tracing using WiFi connectivity data in a passive and privacy-preserving manner.
Specifically, the paper focuses on inferring the user's activities -- which may, in turn, lead to the user's privacy -- via inferences through device activities and network traffic analysis.
Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems.
This paper focuses on the new challenges of privacy that arise in IoT in the context of smart homes.
Cryptography and Security Networking and Internet Architecture