Optimal Piecewise-Based Mechanism for Collecting Bounded Numerical Data under Local Differential Privacy

Numerical data with bounded domains is a common data type in personal devices, such as wearable sensors. While the collection of such data is essential for third-party platforms, it raises significant privacy concerns. Local differential privacy LDP has been shown as a framework providing provabl...

Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation

Achieving differentially private computations in decentralized settings poses significant challenges, particularly regarding accuracy, communication cost, and robustness against information leakage. While cryptographic solutions offer promise, they often suffer from high communication overhead or...

Fine-Grained Manipulation Attacks to Local Differential Privacy Protocols for Data Streams

Local Differential Privacy LDP enables massive data collection and analysis while protecting end users' privacy against untrusted aggregators. It has been applied to various data types e.g., categorical, numerical, and graph data and application settings e.g., static and streaming. Recent finding...