With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

Contrastive learning pre-trains an image encoder using a large amount of unlabeled data
such that the image encoder can be used as a general-purpose feature extractor for various …

Although local differential privacy (LDP) protects individual users' data from inference by an
untrusted data curator, recent studies show that an attacker can launch a data poisoning …

The protocols that satisfy Local Differential Privacy (LDP) enable untrusted third parties to
collect aggregate information about a population without disclosing each user's privacy. In …

Local Differential Privacy (LDP) is now widely adopted in large-scale systems to collect and
analyze sensitive data while preserving users' privacy. However, almost all LDP protocols …

In this paper, we explore data poisoning attacks and their defenses in local differential
privacy (LDP)-based crowdsensing systems. First, we construct data poisoning attacks …

Model accuracy is the traditional metric employed in machine learning (ML) applications.
However, privacy, fairness, and robustness guarantees are crucial as ML algorithms …

The private collection of multiple statistics from a population is a fundamental statistical
problem. One possible approach to realize this is to rely on the local model of differential …

Local differential privacy (LDP), which enables an untrusted server to collect aggregated
statistics from distributed users while protecting the privacy of those users, has been widely …