Task matching is widely used for participant selection in mobile crowdsensing (MCS).
However, accurate task matching relies on collecting a large amount of user information …

Transitioning to a low-/zero-carbon energy ecosystem requires a thorough and accurate
understanding of how energy is consumed on the demand side. To achieve this goal, user …

Web users can gather data from secure endpoints and demonstrate the provenance of
sensitive data to any third party by using privacy-preserving TLS oracles. In practice, privacy …

We design DiStefano: an efficient, maliciously-secure framework for generating private
commitments over TLS-encrypted web traffic, for a designated third-party. DiStefano …

The combination of federated learning and recommender system aims to solve the privacy
problems of recommendation through keeping user data locally at the client device during …

To quantify trade-offs between increasing demand for open data sharing and concerns
about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes …

Integrating adversarial machine learning with Question Answering (QA) systems has
emerged as a critical area for understanding the vulnerabilities and robustness of these …

In many mobile applications, user-generated data are presented as set-valued data. To
tackle potential privacy threats in analyzing these valuable data, local differential privacy has …

Secret Sharing (SS) is widely adopted in secure Multi-Party Computation (MPC) with its
simplicity and computational efficiency. However, SS-based MPC protocol introduces …

In this work, we introduce a lightweight communication-efficient multi-key approach suitable
for the Federated Averaging rule. By combining secret-key RLWE-based HE, additive secret …