This paper provides a comprehensive review of differential privacy (DP) technology and
explores its applications in geotechnical engineering, where data privacy concerns are …

We revisit the problem of designing scalable protocols for private statistics and private
federated learning when each device holds its private data. Locally differentially private …

The modern Internet is built on systems that incentivize collection of information about users.
In order to minimize privacy loss, it is desirable to prevent these systems from collecting …

Private heavy-hitters is a data-collection task where multiple clients possess private bit
strings, and data-collection servers aim to identify the most popular strings without learning …

Graphs are widely used to model the complex relationships among entities. As a powerful
tool for graph analytics, graph neural networks (GNNs) have recently gained wide attention …

Secure aggregation of high-dimensional vectors is a fundamental primitive in federated
statistics and learning. A two-server system such as PRIO allows for scalable aggregation of …

Insight into user experience and behavior is critical to the success of large software systems
and web services. Gaining such insights, while preserving user privacy, is a significant …

In this work, we study practical heuristics to improve the performance of prefix-tree based
algorithms for differentially private heavy hitter detection. Our model assumes each user has …

Streaming graphs have seen wide adoption in diverse scenarios due to their superior ability
to capture temporal interactions among entities. With the proliferation of cloud computing, it …

Analyzing user data while protecting the privacy of individuals remains a big challenge.
Trusted execution environments (TEEs) are a possible solution as they protect processes …