Abstract
The privacy-preserving data release is an increasingly important problem in today’s computing. As the end devices collect more and more data, reducing the amount of published data saves considerable network, CPU and storage resources. The savings are especially important for constrained end devices that collect and send large amounts of data, especially over wireless networks. We propose the use of query-independent, similitude models for privacy-preserving data release on the end devices. The conducted experiments validate that the wavelet-based similitude model maintains an accuracy compared to other state-of-the-art methods while compressing the model. Expanding on our previous work (Derbeko et al. in: Cyber security cryptography and machine learning-second international symposium, CSCML 2018, Beer Sheva, Israel, 2018) we show how wavelet-based similitude models can be combined and “subtracted” when new end devices appear or leave the system. Experiments show that accuracy is the same or improved with a model composition. This data-oriented approach allows further processing near the end devices in a fog or a similar edge computing concept.
Similar content being viewed by others
Notes
The code can be found at http://planete.inrialpes.fr/projects/p-publication/ with our addition at http://github.com/kvikeg/WaveletSimilitudeModel
References
Derbeko, P., Dolev, S., Gudes, E. (2018). Privacy via maintaining small similitude data for big data statistical representation. In Cyber security cryptography and machine learning - second international symposium, CSCML 2018, Beer Sheva, Israel, June 21–22, 2018, Proceedings, pp. 105–119. https://doi.org/10.1007/978-3-319-94147-9_9.
Hosseinian-Far, A., Ramachandran, M., Slack, C. (2018). Emerging trends in cloud computing, big data, fog computing, IOT and smart living.
Mahmoudi, C., Mourlin, F., Battou, A. (2018). Formal definition of edge computing: An emphasis on mobile cloud and IOT composition. In 2018 Third international conference on fog and mobile edge computing (FMEC), pp. 34–42.
Rama, P. C. S. (2018). Fog computing and internet of things (IOT).
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K. P., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. arXiv:1808.05283.
Chalapathi, G. S. S., Chamola, V., Vaish, A., Buyya, R. (2019). Industrial internet of things (IIOT) applications of edge and fog computing: A review and future directions. arXiv:1912.00595
Dustdar, S., Avasalcai, C., Murturi, I. (2019). Invited paper: Edge and fog computing: Vision and research challenges. In 2019 IEEE international conference on service-oriented system engineering (SOSE), pp. 96–9609.
Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10, 557–570.
Samarati, P. (2001). Protecting respondents’ identities in microdata release. IEEE Trans. on Knowl. and Data Eng., 13(6), 1010–1027. https://doi.org/10.1109/69.971193.
Samarati, P., & Sweeney, L. (1998). Generalizing data to provide anonymity when disclosing information (abstract). In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, PODS ’98, p. 188. ACM, New York. https://doi.org/10.1145/275487.275508.
Machanavajjhala, A., Kifer, D., Gehrke, J. (2007).Venkitasubramaniam, M.: L-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery, 1(1) . https://doi.org/10.1145/1217299.1217302.
Wong, R. C. W., Li, J., Fu, A. W. C., Wang, K. (2006). (alpha, 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, KDD ’06, pp. 754–759. ACM, New York. https://doi.org/10.1145/1150402.1150499.
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K. P., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J. P. (2018). All one needs to know about fog computing and related edge computing paradigms: A complete survey. CoRR arXiv:1808.05283.
Regulation of the European parliament on the protection of natural persons with regard to processing of personal data and on the free movement of such data (2018). https://eur-lex.europa.eu/eli/reg/2016/679/oj.
Kline, S. (1986). Similitude and approximation theory. Springer, Berlin.
Blum, A., Ligett, K., Roth, A. (2008). A learning theory approach to non-interactive database privacy. In Proceedings of the fortieth annual ACM symposium on theory of computing, STOC ’08, pp. 609–618. ACM, New York. https://doi.org/10.1145/1374376.1374464.
Ninghui, L., Tiancheng, L., Venkatasubramanian, S. (2007). t-closeness: Privacy beyond k-anonymity and l-diversity. In 2007 IEEE 23rd international conference on data engineering, pp. 106–115.
Dwork, C. (2006). Differential privacy. In ICALP, pp. 1–12.
Dwork, C. (2008). Differential privacy: A survey of results. In TAMC. Springer, Berlin.
Dwork, C., McSherry, F., Nissim, K., Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of cryptography, third theory of cryptography conference, TCC 2006, New York, 2006, Proceedings, pp. 265–284.
Dinur, I., & Nissim, K. (2003). Revealing information while preserving privacy. In PODS, pp. 202–210. ACM Press.
Dwork, C., Rothblum, G., Vadhan, S. (2010). Boosting and differential privacy. In Proceedings of the 51st annual IEEE symposium on foundations of computer science (FOCS’10), p. 51–60. IEEE, IEEE, Las Vegas, NV.
Ullman, J. (2013). Answering n2+O(1) counting queries with differential privacy is hard. In Proceedings of the forty-fifth annual ACM symposium on theory of computing, STOC ’13, pp. 361–370. ACM, New York. https://doi.org/10.1145/2488608.2488653.
Bonomi, F., Milito, R.A., Zhu, J., Addepalli, S. (2012). Fog computing and its role in the internet of things. In MCC@SIGCOMM.
Derbeko, P., Dolev, S., Gudes, E., Ullman, J.D. (2017). Efficient and private approximations of distributed databases calculations. In 2017 IEEE international conference on big data, BigData 2017, Boston, MA, USA, pp. 4487–4496. https://doi.org/10.1109/BigData.2017.8258489.
Friedman, A., & Schuster, A. (2010). Data mining with differential privacy. In KDD’ 10.
Ács, G., Castelluccia, C., Chen, R. (2012). Differentially private histogram publishing through lossy compression. In 2012 IEEE 12th international conference on data mining, pp. 1–10.
Rastogi, V., Nath, S. (2010). Differentially private aggregation of distributed time-series with transformation and encryption. In Proceedings of the 2010 ACM SIGMOD international conference on management of data, SIGMOD ’10, pp. 735–746. ACM, New York. https://doi.org/10.1145/1807167.1807247.
Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K. (2007). Privacy, accuracy, and consistency too: A holistic solution to contingency table release. In Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, PODS ’07, pp. 273–282. ACM, New York. https://doi.org/10.1145/1265530.1265569
Howe, B., Stoyanovich, J., Ping, H., Herman, B., Gee, M. (2017). Synthetic data for social good. CoRR arXiv:1710.08874
Dwork, C., Naor, M., Reingold, O., Rothblum, G. N., Vadhan, S. (2009). On the complexity of differentially private data release: Efficient algorithms and hardness results. In Proceedings of the forty-first annual ACM symposium on theory of computing, STOC ’09, pp. 381–390. ACM, New York, NY. https://doi.org/10.1145/1536414.1536467.
Hardt, M., Ligett, K., McSherry, F. (2012). A simple and practical algorithm for differentially private data release. In F. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (Eds.) Advances in neural information processing systems 25, pp. 2339–2347. Curran Associates, Inc.
Hardt, M., & Rothblum, G. N. (2010). A multiplicative weights mechanism for privacy-preserving data analysis. In 2010 IEEE 51st annual symposium on foundations of computer science, pp. 61–70.
Gaboardi, M., Arias, E. J. G., Hsu, J., Roth, A., Wu, Z. S. (2014). Dual query: Practical private query release for high dimensional data. In E. P. Xing, T. Jebara (Eds.) Proceedings of the 31st international conference on machine learning, proceedings of machine learning research, vol. 32, pp. 1170–1178. PMLR, Bejing, China.
Wang, Q., Chen, D., Zhang, N., Ding, Z., & Qin, Z. (2017). Pcp: A privacy-preserving content-based publish-subscribe scheme with differential privacy in fog computing. IEEE Access, 5, 17962–17974.
Wang, T., Zeng, J., Bhuiyan, M. Z. A., Tian, H., Cai, Y., Chen, Y., et al. (2017). Trajectory privacy preservation based on a fog structure for cloud location services. IEEE Access, 5, 7692–7701.
Yang, M., Zhu, T., Liu, B., Xiang, Y., & Zhou, W. (2018). Machine learning differential privacy with multifunctional aggregation in a fog computing architecture. IEEE Access, 6, 17119–17129.
Du, M., Wang, K., Xia, Z., Zhang, Y. (2018). Differential privacy preserving of training model in wireless big data with edge computing. IEEE transactions on big data, pp. 1–1.
Zhang, J., Wang, J., Zhao, Y., Chen, B. (2019). An efficient federated learning scheme with differential privacy in mobile edge computing. In ICML 2019.
Das, A., Brunschwiler, T. (2019). Privacy is what we care about: Experimental investigation of federated learning on edge devices. arXiv:abs/1911.04559
Andrés, M. E., Bordenabe, N. E., Chatzikokolakis, K., Palamidessi, C. V. (2013). Geo-indistinguishability: Differential privacy for location-based systems. ArXiv abs/1212.1984.
Xiao, Y., & Xiong, L. (2015). Protecting locations with differential privacy under temporal correlations. In CCS ’15.
Zhou, L., Yu, L., Du, S., Zhu, H., & Chen, C. (2019). Achieving differentially private location privacy in edge-assistant connected vehicles. IEEE Internet of Things Journal, 6, 4472–4481.
Zhang, P., Durresi, M., Durresi, A. (2019). Network location privacy protection with multi-access edge computing. In AINA.
Garofalakis, M., & Kumar, A. (2004). Deterministic wavelet thresholding for maximum-error metrics. In Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, PODS ’04, pp. 166–176. ACM, New York. https://doi.org/10.1145/1055558.1055582.
Stollnitz, E. J., Derose, T. D., & Salesin, D. H. (1996). Wavelets for computer graphics: Theory and applications. San Francisco, CA: Morgan Kaufmann Publishers Inc.
Qardaji, W. H., Yang, W., & Li, N. (2013). Understanding hierarchical methods for differentially private histograms. PVLDB, 6, 1954–1965.
Hay, M., Rastogi, V., Miklau, G., Suciu, D. (2010). Boosting the accuracy of differentially private histograms through consistency. In Proceedings of VLDB endowment 3(1-2), 1021–1032 . https://doi.org/10.14778/1920841.1920970.
Xiao, X., Wang, G., Gehrke, J. (2010). Differential privacy via wavelet transforms. In 2010 IEEE 26th international conference on data engineering (ICDE 2010), pp. 225–236.
Haar, A. (1910). Zur theorie der orthogonalen funktionensysteme. Mathematische Annalen, 69, 331–371.
Lichman, M. (2013). UCI machine learning repository. http://archive.ics.uci.edu/ml.
Pywavelets - wavelet transforms in python. Available at https://github.com/PyWavelets/pywt.
AT&T, contributers: Graphviz - graph visualization software. Available at http://graphviz.org.
Matias, Y., Vitter, J. S., & Wang, M. (1998). Wavelet-based histograms for selectivity estimation. SIGMOD Record, 27(2), 448–459. https://doi.org/10.1145/276305.276344.
Chakrabarti, K., Garofalakis, M., Rastogi, R., & Shim, K. (2001). Approximate query processing using wavelets. The VLDB Journal, 10(2–3), 199–223.
Gilbert, A. C., Kotidis, Y., Muthukrishnan, S., Strauss, M. J. (2001). Optimal and approximate computation of summary statistics for range aggregates. In Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, PODS ’01, pp. 227–236. ACM, New York, NY. https://doi.org/10.1145/375551.375598.
Vitter, J. S., & Wang, M. (1999). Approximate computation of multidimensional aggregates of sparse data using wavelets. SIGMOD Record, 28(2), 193–204. https://doi.org/10.1145/304181.304199.
Vitter, J. S., Wang, M., Iyer, B. (1998). Data cube approximation and histograms via wavelets. In Proceedings of the seventh international conference on information and knowledge management, CIKM ’98, pp. 96–104. ACM, New York, NY. https://doi.org/10.1145/288627.288645.
Chakrabarti, K., Garofalakis, M. N., Rastogi, R., & Shim, K. (2000). Approximate query processing using wavelets. The VLDB Journal, 10, 199–223.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Derbeko, P., Dolev, S. & Gudes, E. Wavelet-based dynamic and privacy-preserving similitude data models for edge computing. Wireless Netw 27, 351–366 (2021). https://doi.org/10.1007/s11276-020-02457-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-020-02457-2