A Privacy Preserving Method for IoT Forensics

Wenzheng Zhang, Boxi Chen, Xiao Fu, Qing Gu, Jin Shi, Xiaojiang Du, Xiaoyang Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The diversity of the Internet of Things (IoT) poses challenges to privacy protection, especially in the field of digital forensics. How to ensure that only the private information of the suspect is provided, and not the irrelevant information of other users is disclosed is crucial, especially when obtaining evidence in the complex IoT environment. To the best of our knowledge, there are few studies on protecting the privacy of irrelevant users in the IoT forensics. However, it is very important to ensure that the evidence does not violate the privacy of other users when collecting evidence, because it directly determines whether the evidence is legal and whether it can be admissible in court. In this paper, a new method based on data provenance graph is designed to solve the privacy protection problem of IoT forensics. The key idea of this method is to protect privacy by dividing multi-user information and protecting it from an encryption perspective. The method consists of three main phrases: distinguishing different users' data provenance graphs using traversal search, node abstraction, and hiding techniques, utilizing pseudo-random dual-key negotiation methods tailored for the scenario to enhance privacy protection for unrelated users, and employing identity authentication technology to facilitate better investigation and extraction of data provenance graph information of criminal accomplices in specific scenarios. Example proves that this method has practical significance and promising application prospects in protecting the privacy of unrelated users in IoT forensics while ensuring evidence accessibility in special criminal scenarios.

Original languageEnglish
Title of host publicationGLOBECOM 2024 - 2024 IEEE Global Communications Conference
Pages2407-2412
Number of pages6
ISBN (Electronic)9798350351255
DOIs
StatePublished - 2024
Event2024 IEEE Global Communications Conference, GLOBECOM 2024 - Cape Town, South Africa
Duration: 8 Dec 202412 Dec 2024

Publication series

NameProceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813

Conference

Conference2024 IEEE Global Communications Conference, GLOBECOM 2024
Country/TerritorySouth Africa
CityCape Town
Period8/12/2412/12/24

Keywords

  • IoT forensics
  • data provenance graph
  • digital forensics
  • privacy protection

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