Abstract
The main obstacle to the development of sustainable and productive ecosystems leveraging data is the unavailability of robust, reliable and convenient privacy management tools and services. We propose to demonstrate our Privacy-as-a-Service system and Liánchéng, the Cloud system that hosts it. We consider not only the publication of data but also that of models created by parametric and non-parametric statistical machine learning algorithms. We illustrate the construction and execution of privacy preserving workflows using real-world datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Minnesota population center. Integrated public use microdata series - international: Version 5.0 (2009). https://international.ipums.org
Regulation (EU) 2016/679 general data protection regulation (text with EEA relevance). Official J. Eur. Union L(119), 1–88 (2016). https://eur-lex.europa.eu/eli/reg/2016/679/oj
Dandekar, A., Basu, D., Bressan, S.: Differential privacy for regularised linear regression. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11030, pp. 483–491. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98812-2_44
Dandekar, A., Zen, R.A.M., Bressan, S.: A comparative study of synthetic dataset generation techniques. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11030, pp. 387–395. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98812-2_35
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Hall, R., Rinaldo, A., Wasserman, L.: Differential privacy for functions and functional data. J. Mach. Learn. Res. 14(Feb), 703–727 (2013)
Heyrani-Nobari, G., Boucelma, O., Bressan, S.: Privacy and anonymization as a service: PASS. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5982, pp. 392–395. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12098-5_33
Hundepool, A., et al.: Statistical Disclosure Control. Wiley, Chichester (2012)
Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007, pp. 106–115. IEEE (2007)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 3 (2007)
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. The MIT Press, Cambridge (2012)
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3–18. IEEE (2017)
Smola, A.J., Schölkopf, B.: Learning with Kernels, vol. 4. Citeseer (1998)
Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)
Zare-Mirakabad, M.-R., Jantan, A., Bressan, S.: Privacy risk diagnosis: mining l-diversity. In: Chen, L., Liu, C., Liu, Q., Deng, K. (eds.) DASFAA 2009. LNCS, vol. 5667, pp. 216–230. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04205-8_19
Zhang, J., Zhang, Z., Xiao, X., Yang, Y., Winslett, M.: Functional mechanism: regression analysis under differential privacy. Proc. VLDB Endow. 5(11), 1364–1375 (2012)
Acknowledgement
This project is supported by the National Research Foundation, Singapore Prime Minister’s Office, under its Campus for Research Excellence and Technological Enterprise (CREATE) programme and under its Corporate Laboratory@University Scheme between National University of Singapore and Singapore Telecommunications Ltd.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Dandekar, A., Basu, D., Kister, T., Poh, G.S., Xu, J., Bressan, S. (2019). Privacy as a Service: Publishing Data and Models. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_86
Download citation
DOI: https://doi.org/10.1007/978-3-030-18590-9_86
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18589-3
Online ISBN: 978-3-030-18590-9
eBook Packages: Computer ScienceComputer Science (R0)