A Survey of Secure and Private Data Sketching

PhD Qualifying Examination


Title: "A Survey of Secure and Private Data Sketching"

by

Mr. Jianzhe YU


Abstract:

Sketching algorithms have emerged as a fundamental technique for processing 
massive datasets and high-speed data streams, enabling efficient estimation 
of important statistics. However, as these sketches are increasingly deployed 
in distributed and sensitive environments, ensuring data privacy and security 
has become crucial. This survey provides a comprehensive review of 
privacy-preserving sketching techniques, with a focus on two primary 
approaches: secure multiparty computation (MPC) protocols and differential 
privacy (DP) mechanisms. We review how these techniques have been adapted to 
protect widely adopted sketches for three fundamental estimation problems: 
frequency estimation, cardinality estimation, and quantile estimation. This 
survey discusses the trade-offs between computational complexity, 
communication overhead, and estimation accuracy in these secure protocols and 
private mechanisms, concluding with a discussion on open challenges and 
future research directions.


Date:                   Wednesday, 4 February 2026

Time:                   3:00pm - 5:00pm

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Prof. Ke Yi (Supervisor)
                        Dr. Dimitris Papadopoulos (Chairperson)
                        Dr. Mingxun Zhou