Novel algorithm for efficient privacy preservation in data analytics

P Ram Mohan Rao; S Murali Krishna; A P Siva Kumar

doi:10.17485/IJST/v14i6.1773

Article

Novel algorithm for efficient privacy preservation in data analytics

VIEWS 1438
PDF 398

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i6.1773

Year: 2021, Volume: 14, Issue: 6, Pages: 519-526

Original Article

Novel algorithm for efficient privacy preservation in data analytics

P Ram Mohan Rao^1*, S Murali Krishna², A P Siva Kumar³

¹Department of Computer Science and Engineering, JNTUA, Anantapuramu, Andhra Pradesh, India
²Department of Computer Science and Engineering, Sri Venkateswara College of
Engineering, Tirupathi, Andhra Pradesh, India
³Department of Computer Science and Engineering, JNTUA, Anantapuramu, Andhra Pradesh, India

*Corresponding Author
Email: [email protected]

Received Date:29 September 2020, Accepted Date:12 January 2021, Published Date:19 February 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To address the modern privacy threats in data analytics by designing an efficient privacy preserving data analytics technique. Methods: The method applied is a non anonymized method that uses the concepts of synthesizing quasi identifiers and application of differential privacy. The proposed method was applied to three data sets viz. Adult data set, Statlogdata set and Indian Liver Patient data set. All the data sets are freely available in the UCI repository. Findings: The study presents “Synthesize Quasi Identifiers and apply Differential Privacy” (SQIDP) which is proved to be a more efficient and scalable algorithm. Compared to anonymity based algorithms SQIDP is not prone to similarity attacks, background knowledge attacks, attribute disclosure, and inference attacks. Anonymization, cryptographic, SWARM, and randomization methods will reduce data utility whereas SQIDP offers 100% data utility. Hence it is more efficient than other techniques. SQIDP was applied on three different data sets with 270, 583, and 48842 records but the execution time of the algorithm remained the same for all three data sets. SQIDP is proved to be a better privacy preservation technique with 100% data utility because it is not anonymized that abides by the recommendation in many privacy legislations like GDPR (General Data Protection Regulation) of the European Union and PDP (Personal Data Protection bill) of India.

Keywords: Data privacy; privacy regulations; privacy preservation; synthetic data; differential

References

Madan S. A Literature Analysis on Privacy Preservation Techniques. In: Advances in Computing and Intelligent Systems. (pp. 2020-223) Springer..
Madan S, PG. A privacy preservation model for big data in map-reduced framework based on k-anonymisation and swarm-based algorithms. International Journal of Intelligent Engineering Informatics. 2020;8:38–53. Available from: https://doi.org/10.1504/IJIEI.2020.105433
Eyupoglu C, Aydin M, Zaim A, Sertbas A. An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques. Entropy. 2018;20(5). Available from: https://dx.doi.org/10.3390/e20050373
Nayahi JJV, Kavitha V. Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop. Future Generation Computer Systems. 2017;74:393–408. Available from: https://dx.doi.org/10.1016/j.future.2016.10.022
Rao PRM, Krishna SM, Kumar APS. Privacy preservation techniques in big data analytics: a survey. Journal of Big Data. 2018;5(1). Available from: https://dx.doi.org/10.1186/s40537-018-0141-8
Tang E. A quantum-inspired classical algorithm for recommendation systems. In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. 2019.
Zhou Y, et al. User attitudes and behaviors toward personalized control of privacy settings on smartphones. Concurrency and Computation: Practice and Experience. 2019;31(22). Available from: https://doi.org/10.1002/cpe.4884
Barth S, et al. Putting the privacy paradox to the test: Online privacy and security behaviors among users with technical knowledge, privacy awareness, and financial resources. Telematics and informatics. 2019;41:55–69. Available from: https://doi.org/10.1016/j.tele.2019.03.003
Mavriki P, Karyda M. Automated data-driven profiling: threats for group privacy. Information & Computer Security. 2019;28(2):183–197. Available from: https://dx.doi.org/10.1108/ics-04-2019-0048
Google chrome to end support for third party cookies within two years. Available from: https://www.cnbc.com/2020/01/14/google-chrome-to-end-support-for-third-party-cookies-within-two-years.html
March E, Litten V, Sullivan DH, Ward L. Somebody that I (used to) know: Gender and dimensions of dark personality traits as predictors of intimate partner cyberstalking. Personality and Individual Differences. 2020;163. Available from: https://dx.doi.org/10.1016/j.paid.2020.110084
Chen L, Gong T, Kosinski M, Stillwell D, Davidson RL. Building a profile of subjective well-being for social media users. PLOS ONE. 2017;12(11). Available from: https://dx.doi.org/10.1371/journal.pone.0187278
Yang J, Wu J, Wang X. Convolutional neural network based on differential privacy in exponential attenuation mode for image classification. IET Image Processing. 2020. Available from: https://dx.doi.org/10.1049/iet-ipr.2020.0078
Beaulieu-Jones BK, Wu ZS, Williams C, Lee R, Bhavnani SP, Byrd JB, et al. Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing. Circulation: Cardiovascular Quality and Outcomes. 2019;12(7). Available from: https://dx.doi.org/10.1161/circoutcomes.118.005122
Wang P, Chen T, Wang Z. Research on privacy preserving data mining. Journal of Information Hiding and Privacy Protection. 2019;1(2):61–68. Available from: https://doi.org/10.32604/jihpp.2019.05943
Walters A. Available from: https://patents.google.com/patent/US20200012902A1/en
Dua D, Graff C. 2019. Available from: http://archive.ics.uci.edu/ml
Rubinstein B, Ip F, Alda. Available from: https://cran.r-project.org/web/packages/diffpriv/vignettes/diffpriv.pdf
Abowd JM. The US census bureau adopts differential privacy. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.

Copyright

© 2021 Ram Mohan Rao et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)