Cyber Security Webinar by Dr. Changyu Dong - How to Make Private Distributed Cardinality Estimation Practical, and Get Differential Privacy for Free

15 februari 2022 12:00 t/m 12:45 - Locatie: ZOOM-MEETING | Zet in mijn agenda

Meeting details

Join Zoom Meeting:
https://tudelft.zoom.us/j/91029287570?pwd=aXNZQUtjZlNMUzdiNnhJZU10WUJFQT09 

Meeting ID:
910 2928 7570

Passcode:
259327

Abstract

Secure computation is a promising privacy enhancing technology, but it is often not scalable enough for data intensive applications. On the other hand, the use of sketches has gained popularity in data mining, because sketches often give rise to highly efficient and scalable sub-linear algorithms. It is natural to ask: what if we put secure computation and sketches together?

We investigated the question and the findings are interesting: we can get security, we can get scalability, and somewhat unexpectedly, we can also get differential privacy — for free. Our study started from building a secure computation protocol based on the Flajolet-Martin (FM) sketches, for solving the Private Distributed Cardinality Estimation (PDCE) problem, which is a fundamental problem with applications ranging from crowd tracking to network monitoring.

The state of the art protocol for PDCE is computationally expensive and not scalable enough to cope with big data applications, which prompted us to design a better protocol. Our further analysis revealed that if the cardinality to be estimated is large enough, our protocol can achieve (ϵ,δ)-differential privacy automatically, without requiring any additional manipulation of the output. The result signifies a new approach for achieving differential privacy that departs from the mainstream approach (i.e. adding noise to the result). Free differential privacy can be achieved because of two reasons: secure computation minimizes information leakage, and the intrinsic estimation variance of the FM sketch makes the output of our protocol uncertain. We further show that the result is not just theoretical: the minimal cardinality for differential privacy to hold is only 10^2−10^4 for typical parameters.

Short bio of Dr. Changyu Dong

Changyu Dong is a Senior Lecturer in Security at the School of Computing, Newcastle University. He obtained his PhD from Imperial College London, on trust management in large distributed systems. He has published more than 40 papers in major journals and conferences, including prestigious ACM CCS, USENIX Security, ESORICS and IEEE Transactions on Dependable and Secure Computing (TDSC) and IEEE Transactions on Information Forensics and Security (TIFS). Three of his papers were selected as best paper at international conferences. He has served on the program committees for many conferences and workshops (e.g. ESORICS, IEEE Trustcom) and is a regular invited reviewer for international journals.

His research is motivated by security, privacy and trust issues in distributed systems, and aims to find practical solutions by combining tools from computer science, cryptography, formal methods, and game theory. He has extensive experience on privacy enhancing technologies, cryptographic protocol design and analysis, as well as secure computation.

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