SkillsFuture Series: Privacy-Preserving Technologies 2

Through the 2-day course, participants will be exposed to the four primary technological verticals aid in privacy-preserving data discovery, aggregation, search, mining, and data-driven learning. These technologies are Differential Privacy, Searchable Encryption, Multi-Party Computation and Homomorphic Encryption. After the completion, participants will get an understanding of how these approaches address the challenge from different angles.
 

TOPICS COVERED:
1: Introduction to Privacy-preserving Techniques: Background, Technology, and Use Cases
Explore solutions and plans for privacy-preserving technologies (PP-Tech). Main aims at providing solutions to related industry, adding value to the digital economy, and promoting social adoption.

2: Technology Focus 1 - Homomorphic Encryption
Homomorphic Encryption schemes allow computations on ciphertext, without revealing information on plaintext. We design new fully homomorphic encryption (FHE) constructions based on new mathematical tools to efficiently secure data management on the cloud. To improve the efficiency of existing FHE constructions, we develop efficient computation algorithms on encrypted data.

3: Technology Focus 2 - Secure Multiparty Computation
Multi-Party Computation (MPC) schemes enables multiple parties to collaboratively perform computation without disclosing any party's private input. We design and implement secure MPC based on FHE, speed up the MPC of particular functions in terms of rounds, improve communication and computation complexities.

4: Technology Focus 3 - Searchable Encryption
Symmetrical Searchable Encryption (SSE) schemes allow ciphertext searching which enables data to be securely stored at the cloud. Our work focuses on building flexible SSE schemes for ease of configuration in the level of security, search time, storage for application in practical large databases. We design leakage-resilient provable secure frameworks to maximize query capacity and improve existing structured encryption schemes to achieve performance-balanced expressive SSE.

5: Technology Focus 4 - Differential Privacy and its Applications
Differential Privacy mechanism can be used to satisfy data aggregation without leaking individual private information. These algorithms rely on incorporating random noise into the data.