CRSep 11, 2021

A Privacy-Preserving Logistics Information System with Traceability

arXiv:2109.05216v1
Originality Synthesis-oriented
AI Analysis

This addresses privacy concerns for users in logistics systems, but it is incremental as it builds on existing cryptographic techniques like multi-signature and pseudonyms.

The paper tackled the problem of privacy protection in Logistics Information Systems (LIS) by proposing a privacy-preserving LIS with traceability (PPLIST), which uses pseudonyms and multi-signature to protect user data while allowing traceability in case of abnormalities.

Logistics Information System (LIS) is an interactive system that provides information for logistics managers to monitor and track logistics business. In recent years, with the rise of online shopping, LIS is becoming increasingly important. However, since the lack of effective protection of personal information, privacy protection issue has become the most problem concerned by users. Some data breach events in LIS released users' personal information, including address, phone number, transaction details, etc. In this paper, to protect users' privacy in LIS, a privacy-preserving LIS with traceability (PPLIST) is proposed by combining multi-signature with pseudonym. In our PPLIST scheme, to protect privacy, each user can generate and use different pseudonyms in different logistics services. The processing of one logistics is recorded and unforgeable. Additionally, if the logistics information is abnormal, a trace party can de-anonymize users, and find their real identities. Therefore, our PPLIST efficiently balances the relationship between privacy and traceability.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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