TY - GEN
T1 - dK-Projection
T2 - 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
AU - Iftikhar, Masooma
AU - Wang, Qing
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Network data has great significance for commercial and research purposes. However, most networks contain sensitive information about individuals, thereby requiring privacy-preserving mechanisms to publish network data while preserving data utility. In this paper, we study the problem of publishing higher-order network statistics, i.e., joint degree distribution, under strong mathematical guarantees of node differential privacy. This problem is known to be challenging, since even simple network statistics (e.g., edge count) can be highly sensitive to adding or removing a single node in a network. To address this challenge, we propose a general framework of publishing dK-distributions under node differential privacy, and develop a novel graph projection algorithm to transform graphs to θ -bounded graphs for controlled sensitivity. We have conducted experiments to verify the utility enhancement and privacy guarantee of our proposed framework on four real-world networks. To the best of our knowledge, this is the first study to publish higher-order network statistics under node differential privacy, while enhancing network data utility.
AB - Network data has great significance for commercial and research purposes. However, most networks contain sensitive information about individuals, thereby requiring privacy-preserving mechanisms to publish network data while preserving data utility. In this paper, we study the problem of publishing higher-order network statistics, i.e., joint degree distribution, under strong mathematical guarantees of node differential privacy. This problem is known to be challenging, since even simple network statistics (e.g., edge count) can be highly sensitive to adding or removing a single node in a network. To address this challenge, we propose a general framework of publishing dK-distributions under node differential privacy, and develop a novel graph projection algorithm to transform graphs to θ -bounded graphs for controlled sensitivity. We have conducted experiments to verify the utility enhancement and privacy guarantee of our proposed framework on four real-world networks. To the best of our knowledge, this is the first study to publish higher-order network statistics under node differential privacy, while enhancing network data utility.
KW - Data publishing
KW - Node differential privacy
KW - dK-distributions
UR - http://www.scopus.com/inward/record.url?scp=85111169568&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-75765-6_29
DO - 10.1007/978-3-030-75765-6_29
M3 - Conference contribution
SN - 9783030757649
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 358
EP - 370
BT - Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Proceedings
A2 - Karlapalem, Kamal
A2 - Cheng, Hong
A2 - Ramakrishnan, Naren
A2 - Agrawal, R. K.
A2 - Reddy, P. Krishna
A2 - Srivastava, Jaideep
A2 - Chakraborty, Tanmoy
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 May 2021 through 14 May 2021
ER -