Qingqing Ye

Mobile Data Collection and Analysis with Local Differential Privacy

2019

Local Differential Privacy (LDP), where each user perturbs her data locally before sending to an untrusted party, is a new and promising privacy-preserving model for mobile data collection and analysis. LDP has been deployed in …

Protecting Decision Boundary of Machine Learning Model With Differentially Private Perturbation

2020 · IEEE Transactions on Dependable and Secure Computing

Machine learning service API allows model owners to monetize proprietary models by offering prediction services to third-party users. However, existing literature shows that model parameters are vulnerable to extraction attacks which accumulate prediction queries and …

DDRM: A Continual Frequency Estimation Mechanism with Local Differential Privacy

2022 · IEEE Transactions on Knowledge and Data Engineering

Many applications rely on continual data collection to provide real-time information services, e.g., real-time road traffic forecasts. However, the collection of original data brings risks to user privacy. Recently, local differential privacy (LDP) has emerged …

Privacy for Free: Leveraging Local Differential Privacy Perturbed Data from Multiple Services

2025 · Proceedings of the VLDB Endowment

Local Differential Privacy (LDP) has emerged as a widely adopted privacy-preserving technique in modern data analytics, enabling users to share statistical insights while maintaining robust privacy guarantees. However, current LDP applications assume a single service …