TY - GEN
T1 - A Privacy Preserving Method for IoT Forensics
AU - Zhang, Wenzheng
AU - Chen, Boxi
AU - Fu, Xiao
AU - Gu, Qing
AU - Shi, Jin
AU - Du, Xiaojiang
AU - Zhou, Xiaoyang
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - IoT forensics
KW - data provenance graph
KW - digital forensics
KW - privacy protection
UR - http://www.scopus.com/inward/record.url?scp=105000829290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105000829290&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM52923.2024.10900998
DO - 10.1109/GLOBECOM52923.2024.10900998
M3 - Conference contribution
AN - SCOPUS:105000829290
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 2407
EP - 2412
BT - GLOBECOM 2024 - 2024 IEEE Global Communications Conference
T2 - 2024 IEEE Global Communications Conference, GLOBECOM 2024
Y2 - 8 December 2024 through 12 December 2024
ER -