Optimized Deep Learning for Enhanced Trade-off in Differentially Private Learning
Received: 30 December 2020 | Accepted: 11 January 2021 | Online: 6 February 2021
Corresponding author: P. Geetha
Abstract
Privacy and data analytics are two conflicting domains that have gained interest due to the advancements of technology in the big data era. Organizations in sectors such as finance, healthcare, and e-commerce take advantage of the data collected, to help them enable innovative decision making and analysis. What is sidelined is the fact that the collected data have associated private data of the individuals involved, and may be exploited and used for unjustified purposes. Defending privacy and performing useful analytics are two sides of the same coin, and hence achieving a good balance between these is a challenging scenario. This paper proposes an optimized differentially private deep learning mechanism that enhances the trade-off between the conflicting objectives of privacy, accuracy, and performance. The goal of this paper is to provide an optimal solution that gives a quantifiable trade-off between these contradictory objectives.
Keywords:
privacy, optimization, pareto-optimal, analyticsDownloads
References
L. Sweeney, "Achieving k-anonymity privacy protection using generalization and suppression," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 571-588, Oct. 2002. https://doi.org/10.1142/S021848850200165X
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, "L-diversity: Privacy beyond k-anonymity," ACM Transactions on Knowledge Discovery from Data, vol. 1, no. 1, Mar. 2007, Art. no. 3-es. https://doi.org/10.1145/1217299.1217302
C. Dwork and A. Roth, "The Algorithmic Foundations of Differential Privacy," Foundations and Trends® in Theoretical Computer Science, vol. 9, no. 3-4, pp. 211-407, Aug. 2014. https://doi.org/10.1561/0400000042
I. Mironov, "Renyi Differential Privacy," in 2017 IEEE 30th Computer Security Foundations Symposium (CSF), Aug. 2017, pp. 263-275. https://doi.org/10.1109/CSF.2017.11
"Google's Differential Privacy May be Better Than Apple's," The Mac Observer, Sep. 15, 2017. https://www.macobserver.com/analysis/
google-apple-differential-privacy/ (accessed Jan. 17, 2021).
M. Abadi et al., "Deep Learning with Differential Privacy," in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, New York, NY, USA, Oct. 2016, pp. 308-318. https://doi.org/10.1145/2976749.2978318
G. Naya, "Available hyperparameter optimization techniques," Medium, Jan. 08, 2020. https://towardsdatascience.com/available-hyperparameter-optimization-techniques-dc60fb836264 (accessed Jan. 17, 2021).
N. Gunantara, "A review of multi-objective optimization: Methods and its applications," Cogent Engineering, vol. 5, no. 1, p. 1502242, Jan. 2018. https://doi.org/10.1080/23311916.2018.1502242
I. A. Kandhro, S. Z. Jumani, F. Ali, Z. U. Shaikh, M. A. Arain, and A. A. Shaikh, "Performance Analysis of Hyperparameters on a Sentiment Analysis Model," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6016-6020, Aug. 2020. https://doi.org/10.48084/etasr.3549
V. Kumar and S. K. Dhull, "Genetic Algorithm based Optimization of Uniform Circular Array," Engineering, Technology & Applied Science Research, vol. 10, no. 6, pp. 6403-6409, Dec. 2020. https://doi.org/10.48084/etasr.3792
B. Avent, J. Gonzalez, T. Diethe, A. Paleyes, and B. Balle, "Automatic Discovery of Privacy-Utility Pareto Fronts," in Proceedings on Privacy Enhancing Technologies 2020, Jul. 2020, vol. 4, pp. 5-23, Accessed: Jan. 17, 2021. [Online]. Available: http://arxiv.org/abs/1905.10862. https://doi.org/10.2478/popets-2020-0060
R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, Sep. 2015, pp. 909-910. https://doi.org/10.1109/ALLERTON.2015.7447103
"Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine | WIRED." https://www.wired.com/2015/11/google-open-sources-its-artificial-intelligence-engine/ (accessed Jan. 17, 2021).
D. Dua and C. Graff, UCI Machine Learning Repository. Irvine, CA, USA: University of California, School of Information and Computer Science, 2019.
R. Iyengar, J. P. Near, D. Song, O. Thakkar, A. Thakurta, and L. Wang, "Towards Practical Differentially Private Convex Optimization," in 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, May 2019, pp. 299-316. https://doi.org/10.1109/SP.2019.00001
T. Handhayani, J. Hendryli, and L. Hiryanto, "Comparison of shallow and deep learning models for classification of Lasem batik patterns," in 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, Nov. 2017, pp. 11-16. https://doi.org/10.1109/ICICOS.2017.8276330
Downloads
How to Cite
License
Copyright (c) 2021 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
