Abstract
In recent years, mobile devices have become increasingly more popular with the rapid development of the Internet and information technology. A large amount of high-dimensional data, which is stored in a distributed manner in multiple agencies, has become a very important resource on the current Internet. Collecting these data for analysis and applications contributes to great social and economic value. Therefore, the privacy problem of high-dimensional data publication in distributed multiparty settings has drawn progressively more attention and become one of the popular issues in current research. The existing anonymous methods mainly adopt the k-anonymous model or differential privacy, which apply a uniform threshold to add noise. However, different attributes in the dataset often have different sensitivities. Additionally, the correlation between attributes also increases the risk of privacy leakage. Moreover, the allocation of a privacy budget in differential privacy is another problem. To solve these problems, this paper proposes a multidimensional correlation hierarchical differential privacy (MuCH-DP) method in the medical data publication domain with multiple privacy requirements. In a distributed multiparty setting, the correlation between attributes is quantified through mutual information and established by the relevant Bayesian networks. To guarantee privacy and improve data utility, this paper designs a personalized privacy budget allocation strategy for the different sensitivities and assigns personalized privacy budgets for multiple participants. Finally, the feasibility and utility of the multidimensional correlation hierarchical differential privacy (MuCH-DP) method are verified by the experiments.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
L. Wang, W.P. Wang D. Meng. Privacy data publishing based on weighted bayesian networks. Comput. Res. Dev. 53(10), 2343–2353 (2016)
L. Sweeney, k-anonymity: a model for protecting privacy. Int. J. Uncertain., Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)
L. Sweeney, Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertain., Fuzziness Knowl.-Based Syst. 10(5), 571–588 (2002)
R.C.W. Wong, J. Li, A.W.C. Fu, et al. (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing, in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. (ACM, 2006), pp. 754–759
Xiao, X.,Tao, Y. Personalized privacy preservation, in Proceeding of the 2006 International Confirence on Management of Data (ACM, New York, 2006), pp. 229–240.
A. Machanavajjhala, D. Kifer, J. Gehrke, et al. l-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data, 1(1), Article 3 (2007)
N. Li, T. Li, S. Venkatasubramanian, t-closeness: Privacy beyond k-anonymity and l-diversity, in Proceedings of the 23rd International Conference on Data (IEEE, Priscataway, NJ 2007), pp. 106–115.
K. Muralidhar, R. Sarathy, Security of random data perturbation methods. ACM Trans. Database Syst. 24(4), 487–493 (1999)
H. Kargupta, S. Data, Q. Wang, et al. On the privacy preserving properties of random data perturbation techniques, in Proceeding of the 3rd International Conference on Data Mining (IEEE, Piscataway, NJ, 2003), pp. 99–106.
K. Chen, L. Liu. Privacy preserving data classification with rotation perturbation, in Proceeding of the 5rd International Conference on Data Mining (IEEE, Piscataway, NJ, 2005), Article 4
C.C. Aggarwal, S.Y. Philip, A condensation approach to privacy preserving data mining, in Proceeding of International Conference on Extending Database Technology (Springer, Berlin, 2004), pp. 183–199.
C.C. Aggarwal, P.S. Yu, On static and dynamic methods for condensation-based privacy-preserving data mining. ACM Trans. Database Syst. 33(1), 41–79 (2008)
X. Zhang, Y. Xu, X. Wang, Differential privacy data release through adding noise on average value, in Network and Systems Security (Springer, Berlin, 2012), pp. 417–429
C. Dwork, F. McSherry, K. Nissim, et al., Calibrating noise to sensitivity in private data analysis, in Theory of Cryptography (Springer, Berlin, 2012), pp. 265–284.
M. Li, K. Sampigethaya, L. Huang, et al., Swing & swap: user-centric approaches towards maximizing location privacy, in Proceeding of the 5th ACM Workshop on Privacy in Electronic Society (ACM, New York, 2006), pp. 19–28
C. Dwork, Differential privacy, in Proceedings of the 33rd International Colloquium on Automata, Languages and Programming (ICALP). (Venice, Italy, 2006), pp. 1–12.
S. Zhong, Z. Yang, R.N. Wright, Privacy-enhancing kanonymization of customer data, in PODS (2005), pp. 139–147
W. Jiang, C. Clifton, A secure distributed framework for achieving k-anonymity. VLDB J. 15(4), 316–333 (2006)
N. Mohammed, B.C.M. Fung, M. Debbabi, Anonymity meets game theory: secure data integration with malicious participants. VLDB J. 20(4), 567–588 (2011)
N. Mohammed, B.C.M. Fung, P.C.K. Hung, C. Lee, Centralized and distributed anonymization for high-dimensional healthcare data. TKDD 4(4) (2010) Article 18
S. Su, P. Tang, X. Cheng, R. Chen, Z. Wu, Differentially private multi-party high-dimensional data publishing, in ICDE (2016), pp. 205–216
D. Alhadidi, N. Mohammed, B.C.M. Fung, M. Debbabi, Secure distributed famework for achieving ε-differential privacy, in PETS (2012), pp. 120–139
Y. Hong, J. Vaidya, H. Lu, P. Karras, S. Goel, Collaborative search log sanitization: Toward differential privacy and boosted utility. TDSC 12(5), 504–518 (2015)
J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao, PrivBayes: private data release via bayesian networks, in SIGMOD (2014), pp. 1423–1434
Xiao, X., Wang, G., Gehrke, J. (2010) Differential privacy via wavelet transforms, in lCDE (2010), pp. 225–236
R. Chen, Q. Xiao, Y Zhang, J. Xu, Differentially private high-dimensional data publication via sampling-based inference, in SIGKDD (2015), pp. 129–138
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F McSherry, K. Talwar. Privacy, accuracy, and consistency too: a holistic solution to contingency table release, in PODS (2007), pp. 273–282
W.H. Qardaji, W. Yang, N. Li, Priview: practical differentially private release of marginal contingency tables, in SIGMOD (2014), pp. 1435–1446
G. Yaroslavtsev, G. Cormode, C.M. Procopiuc, D. Srivastava, Accurate and effcient private release of datacubes and contingency tables, in ICDE (2013), pp. 745–756
Acknowledgements
The research is supported by the National Science Foundation of China (Nos. 61672176, 61662008, 61502111), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, the Guangxi Collaborative Center of Multi-source Information Integration and Intelligent Processing, Guangxi Natural Science Foundation (No. 2015GXNSFBA139246), and the Innovation Project of Guangxi Graduate Education (No. YCSZ2015104).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, X., Zhao, H., Yu, D., Wang, Le., Liu, P. (2019). Multidimensional Correlation Hierarchical Differential Privacy for Medical Data with Multiple Privacy Requirements. In: Wu, C., Chyu, MC., Lloret, J., Li, X. (eds) Proceedings of the 2nd International Conference on Healthcare Science and Engineering . ICHSE 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-6837-0_12
Download citation
DOI: https://doi.org/10.1007/978-981-13-6837-0_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6836-3
Online ISBN: 978-981-13-6837-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)