April 3, 2024, 4:10 a.m. | Fei Wei, Ergute Bao, Xiaokui Xiao, Yin Yang, Bolin Ding

cs.CR updates on arXiv.org arxiv.org

arXiv:2404.01625v1 Announce Type: new
Abstract: Local differential privacy (LDP) is a strong privacy standard that has been adopted by popular software systems. The main idea is that each individual perturbs their own data locally, and only submits the resulting noisy version to a data aggregator. Although much effort has been devoted to computing various types of aggregates and building machine learning applications under LDP, research on fundamental perturbation mechanisms has not achieved significant improvement in recent years. Towards a more …

arxiv cs.cr data differential privacy idea local locally main mechanism noisy own popular privacy private software software systems standard systems version

Security Analyst

@ Northwestern Memorial Healthcare | Chicago, IL, United States

GRC Analyst

@ Richemont | Shelton, CT, US

Security Specialist

@ Peraton | Government Site, MD, United States

Information Assurance Security Specialist (IASS)

@ OBXtek Inc. | United States

Cyber Security Technology Analyst

@ Airbus | Bengaluru (Airbus)

Vice President, Cyber Operations Engineer

@ BlackRock | LO9-London - Drapers Gardens