Government agencies typically need to take potential risks of disclosure into account
whenever they publish statistics based on their data or give external researchers access to …

The literature on differential privacy almost invariably assumes that the data to be analyzed
are fully observed. In most practical applications this is an unrealistic assumption. A popular …

The widespread use of machine learning in location-related scenarios is propelling the rapid
development of intelligent transportation. To assist users in making more informed travel …

Local differential privacy is a strict definition used to protect respondents in a distributed
statistical survey against an untrusted survey organizer. Respondents randomize their …

Despite several works that succeed in generating synthetic data with differential privacy (DP)
guarantees, they are inadequate for generating high-quality synthetic data when the input …

Multiple imputation (MI) has been widely applied to missing value problems in biomedical,
social and econometric research, in order to avoid improper inference in the downstream …

In this chapter we take up the problem of machine learning for private or sensitive data. The
phrase “privacy-preserving machine learning” can refer to myriad models for privacy and …

As data continues to grow rapidly in volume and complexity, there is an increasing need to
extract meaningful insights from it. These datasets often contain sensitive individual …

A Complete Bibliography of ACM Transactions on Privacy and Security (TOPS) Page 1 A
Complete Bibliography of ACM Transactions on Privacy and Security (TOPS) Nelson HF …