TY - GEN
T1 - An arithmetic differential privacy budget allocation method for the partitioning and publishing of location information
AU - Yan, Yan
AU - Gao, Xin
AU - Mahmood, Adnan
AU - Zhang, Yang
AU - Wang, Shuang
AU - Sheng, Quan Z.
N1 - Funding Information:
The research-at-hand is duly supported by National Nature Science Foundation of China (No. 61762059) and the Science Foundation Ireland’s Research Centre for Future Networks and Communications (CONNECT).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - The rapid development of mobile Internet services and the wide application of intelligent terminals has accelerated the advent of the promising era of big data. A number of big data services based on location information bring convenience to users, however, it also results in serious leakage of personal privacy. The partitioning and publishing method combined with the differential privacy model can provide better range counting query results under the premise of ensuring the privacy of users' location. Nevertheless, most of the existing research studies only focus on the structural design during the partitioning process of location big data and ignore the impact of differential privacy budget allocation methods on the published results. This paper, therefore, proposes an efficient arithmetic privacy budget allocation strategy for the tree-based partitioning and publishing of location big data which satisfies the varepsilon-differential privacy. Experimental results over a large number of real-world datasets prove that the proposed privacy budget allocation method is superior in contrast to the existing methods for improving the usability of the published data.
AB - The rapid development of mobile Internet services and the wide application of intelligent terminals has accelerated the advent of the promising era of big data. A number of big data services based on location information bring convenience to users, however, it also results in serious leakage of personal privacy. The partitioning and publishing method combined with the differential privacy model can provide better range counting query results under the premise of ensuring the privacy of users' location. Nevertheless, most of the existing research studies only focus on the structural design during the partitioning process of location big data and ignore the impact of differential privacy budget allocation methods on the published results. This paper, therefore, proposes an efficient arithmetic privacy budget allocation strategy for the tree-based partitioning and publishing of location big data which satisfies the varepsilon-differential privacy. Experimental results over a large number of real-world datasets prove that the proposed privacy budget allocation method is superior in contrast to the existing methods for improving the usability of the published data.
KW - Differential privacy
KW - Location privacy
KW - Privacy budget allocation
KW - Sequential composition
UR - http://www.scopus.com/inward/record.url?scp=85101205467&partnerID=8YFLogxK
U2 - 10.1109/TrustCom50675.2020.00188
DO - 10.1109/TrustCom50675.2020.00188
M3 - Conference contribution
AN - SCOPUS:85101205467
T3 - Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
SP - 1395
EP - 1401
BT - Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
A2 - Wang, Guojun
A2 - Ko, Ryan
A2 - Bhuiyan, Md Zakirul Alam
A2 - Pan, Yi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
Y2 - 29 December 2020 through 1 January 2021
ER -