Cyber Security Webinar by Tianyu Li MSc - Privacy-Preserving Bin-Packing with Differential Privacy

29 maart 2022 12:00 t/m 12:45 - Locatie: Zoom meeting | Zet in mijn agenda

Meeting details

https://tudelft.zoom.us/j/95110224534?pwd=U3lTUEh1aVZHc21HS1p1NUh1RDR4Zz09

Meeting ID: 951 1022 4534
Passcode: 642557

Abstract

With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service. Information leakage during transportation is an important reason for theft since thieves can identify which truck is the target that contains the valuable products. In this paper, we address the privacy and security issues in bin-packing, which is an algorithm used in delivery centres to determine which packages should be loaded together to a certain truck. Data such as the weight of the packages is needed when assigning items into trucks, which can be called bins.

However, the information is sensitive and can be used to identify the contents in the package. To provide security and privacy during bin-packing, we propose two different privacy-preserving data publishing methods. Both approaches use differential privacy (DP) to hide the existence of any specific package to prevent it from being identified by malicious users. The first approach combines differential privacy with k-anonymity, and the other one applies clustering before differential privacy. Our extensive analyses and experimental results clearly show that our proposed approaches have better privacy guarantees, better efficiency, and better performance than the existing works that use either differential privacy or k-anonymity.

Short bio

Tianyu Li is currently a PhD candidate at the Cyber Security Group of the EEMCS faculty. He received his MSc degree in Informatics at the University of Edinburgh, the United Kingdom, and BEng degree in Information Security at Shanghai Jiao Tong University, China. His research focuses on privacy-preserving techniques using differential privacy and cryptographic tools.

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