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DP-UserPro: differentially private user profile construction and publication |
Zheng HUO1, Ping HE1, Lisha HU1, Huanyu ZHAO2,3( ) |
1. Information Technology School, Hebei University of Economics and Business, Shijiazhuang 050061, China
2. The Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang 050051, China
3. Hebei Authentication Technology Engineering Research Center, Shijiazhuang 050051, China |
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Abstract User profiles are widely used in the age of big data. However, generating and releasing user profiles may cause serious privacy leakage, since a large number of personal data are collected and analyzed. In this paper, we propose a differentially private user profile construction method DP-UserPro, which is composed of DP-CLIQUE and privately top-k tags selection. DP-CLIQUE is a differentially private high dimensional data cluster algorithm based on CLIQUE. The multidimensional tag space is divided into cells, Laplace noises are added into the count value of each cell. Based on the breadthfirst-search, the largest connected dense cells are clustered into a cluster. Then a privately top-k tags selection approach is proposed based on the score function of each tag, to select the most important k tags which can represent the characteristics of the cluster. Privacy and utility of DP-UserPro are theoretically analyzed and experimentally evaluated in the last. Comparison experiments are carried out with Tag Suppression algorithm on two real datasets, to measure the False Negative Rate (FNR) and precision. The results show that DP-UserPro outperforms Tag Suppression by 62.5% in the best case and 14.25% in the worst case on FNR, and DP-UserPro is about 21.1% better on precision than that of Tag Suppression, in average.
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| Keywords
user profile
DP-CLIQUE
clustering
differential privacy
recommender system
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Corresponding Author(s):
Huanyu ZHAO
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Just Accepted Date: 13 May 2020
Issue Date: 12 July 2021
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| 1 |
Y Pan, D Wu. Personalized Online-to-Offline (O2O) service recommendation based on a novel frequent service-set network. IEEE Systems Journal, 2019,13(2): 1599–1607
https://doi.org/10.1109/JSYST.2018.2883214
|
| 2 |
D Chae, S Kim, J Lee. Autoencoder-based personalized ranking framework unifying explicit and implicit feedback for accurate top-N recommendation. Knowledge-Based Systems, 2019, 176: 110–121
https://doi.org/10.1016/j.knosys.2019.03.026
|
| 3 |
X Wang, T L Yang, L Kuang, X Liu, Q Zhang, J M Deen. A tensor-based big-data-driven routing recommendation approach for heterogeneous networks. IEEE Network, 2019, 33(1): 64–69
https://doi.org/10.1109/MNET.2018.1800192
|
| 4 |
Y Liu, A Liu, X Liu, X Huang. A statistical approach to participant selection in location-based social networks for offline event marketing. Information Sciences, 2019, 480: 90–108
https://doi.org/10.1016/j.ins.2018.12.028
|
| 5 |
D Li, Q Lv, L Shang, N Gu. Efficient privacy-preserving content recommendation for online social communities. Neurocomputing, 2017, 219: 440–454
https://doi.org/10.1016/j.neucom.2016.09.059
|
| 6 |
Y Zhao, D Li, Q Lv, L Shang. A scalable algorithm for privacypreserving item-based top-N recommendation. 2018, arXiv Preprint arXiv:1811.02217
|
| 7 |
F J Canny. Collaborative filtering with privacy via factor analysis. In: Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval. 2002, 238–245
https://doi.org/10.1145/564376.564419
|
| 8 |
J Zhan, L C Hsieh, C I Wang, T S Hsu, C J Liau, D W Wang. Privacypreserving collaborative recommender systems. IEEE Transactions on Systems, Man, and Cybernetics, 2010, 40(4): 472–476
https://doi.org/10.1109/TSMCC.2010.2040275
|
| 9 |
S Berkovsky, Y Eytani, T Kuflik, F Ricci. Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: Proceedings of ACM Conference on Recommender Systems. 2007, 9–16
https://doi.org/10.1145/1297231.1297234
|
| 10 |
R Parameswaran, D Blough. Privacy preserving collaborative filtering using data obfuscation. In: Proceedings of IEEE International Conference on Granular Computing. 2007, 380–386
https://doi.org/10.1109/GrC.2007.133
|
| 11 |
H Polat, W Du. Privacy-preserving collaborative filtering using randomized perturbation techniques. In: Proceedings of IEEE International Conference on Data Mining. 2003, 625–628
|
| 12 |
F McSherry, I Mironov. Differentially private recommender systems: building privacy into the net. In: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2009, 627–636
https://doi.org/10.1145/1557019.1557090
|
| 13 |
A Machanavajjhala, A Korolova, D A Sarma. Personalized social recommendations accurate or private? Proceedings of the VLDB Endowment, 2011, 4(7): 440–450
https://doi.org/10.14778/1988776.1988780
|
| 14 |
P J Arnau, A Perego, E Ferrari, J Forne, D Rebollo-Monedero. Privacypreserving enhanced collaborative tagging. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(1): 180–193
https://doi.org/10.1109/TKDE.2012.248
|
| 15 |
T Zhu, G Li, W Zhou, P Xiong, C Yuan. Privacy-preserving topic model for tagging recommender systems. Knowledge and Information Systems, 2016, 46(1):33–58
https://doi.org/10.1007/s10115-015-0832-9
|
| 16 |
G Cormode, M Procopiuc, E Shen, E Shen, T Yu. Differentially private spatial decompositions. In: Proceedings of International Conference on Data Engineering. 2012, 20–31
https://doi.org/10.1109/ICDE.2012.16
|
| 17 |
W Wahbeh, W Yang, N Li. Differentially private grids for geospatial data. In: Proceedings of International Conference on Data Engineering. 2012, 757–768
|
| 18 |
C Dwork, F Mesherry, A Smith. Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd Theory of Cryptography Conference. 2006, 265–284
https://doi.org/10.1007/11681878_14
|
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