A bidirectional reversible and multilevel location privacy protection method based on attribute encryption

Various methods such as k-anonymity and differential privacy have been proposed to safeguard users' private information in the publication of location service data. However, these typically employ a rigid "all-or-nothing" privacy standard that fails to accommodate users' more nuanced and multi-level privacy-related needs. Data is irrecoverable once anonymized, leading to a permanent reduction in location data quality, in turn significantly diminishing data utility. In the paper, a novel, bidirectional and multi-layered location privacy protection method based on attribute encryption is proposed. This method offers layered, reversible, and fine-grained privacy safeguards. A hierarchical privacy protection scheme incorporates various layers of dummy information, using an access structure tree to encrypt identifiers for these dummies. Multi-level location privacy protection is achieved after adding varying amounts of dummy information at different hierarchical levels N. This allows for precise control over the de-anonymization process, where users may adjust the granularity of anonymized data based on their own trust levels for multi-level location privacy protection. This method includes an access policy which functions via an attribute encryption-based access control system, generating decryption keys for data identifiers according to user attributes, facilitating a reversible transformation between data anonymity and de-anonymity. The complexities associated with key generation, distribution, and management are thus markedly reduced. Experimental comparisons with existing methods demonstrate that the proposed method effectively balances service quality and location privacy, providing users with multi-level and reversible privacy protection services.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2024-09-06
DOI
https://doi.org/10.1371/journal.pone.0309990
Citations
1
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.35

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A bidirectional reversible and multilevel location privacy protection method based on attribute encryption

Kaiyi Hu, Milu Md Khaled Hasan, Zhaowei Hu
1 citations
PLoS ONE
Privacy-Preserving Technologies in Data
0.35
article

A bidirectional reversible and multilevel location privacy protection method based on attribute encryption

Kaiyi Hu, Milu Md Khaled Hasan, Zhaowei Hu
article en
1 citations

Abstract

Various methods such as k-anonymity and differential privacy have been proposed to safeguard users' private information in the publication of location service data. However, these typically employ a rigid "all-or-nothing" privacy standard that fails to accommodate users' more nuanced and multi-level privacy-related needs. Data is irrecoverable once anonymized, leading to a permanent reduction in location data quality, in turn significantly diminishing data utility. In the paper, a novel, bidirectional and multi-layered location privacy protection method based on attribute encryption is proposed. This method offers layered, reversible, and fine-grained privacy safeguards. A hierarchical privacy protection scheme incorporates various layers of dummy information, using an access structure tree to encrypt identifiers for these dummies. Multi-level location privacy protection is achieved after adding varying amounts of dummy information at different hierarchical levels N. This allows for precise control over the de-anonymization process, where users may adjust the granularity of anonymized data based on their own trust levels for multi-level location privacy protection. This method includes an access policy which functions via an attribute encryption-based access control system, generating decryption keys for data identifiers according to user attributes, facilitating a reversible transformation between data anonymity and de-anonymity. The complexities associated with key generation, distribution, and management are thus markedly reduced. Experimental comparisons with existing methods demonstrate that the proposed method effectively balances service quality and location privacy, providing users with multi-level and reversible privacy protection services.

PLoS ONEVol. 19(9)
Changzhou Institute of Technology (CN), Changzhou University (CN)
Changzhou University
Peace, Justice and strong institutions
Openalex Percentile: Top 34%
Privacy-Preserving Technologies in Data
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.