Training models for the enterprise - A privacy preserving approach
Keywords:
privacy-preserving model training, enterprise data security, differential privacyAbstract
In today's data-driven landscape, enterprises increasingly rely on machine learning models to extract insights and drive decision-making. However, the growing concern for data privacy presents significant challenges in training these models, especially when sensitive information is involved. This project explores innovative strategies for developing machine learning models that prioritize privacy while maintaining performance and accuracy. Organizations can train models on decentralized data sources without exposing the underlying sensitive data by leveraging techniques such as federated learning, differential privacy, and homomorphic encryption. This approach mitigates the risks associated with data breaches and aligns with regulatory requirements surrounding data protection. The focus is on creating a framework that allows businesses to harness the power of their data while preserving individual privacy. This work illustrates the feasibility of privacy-preserving techniques in various enterprise contexts through practical case studies and real-world applications. It highlights their potential to transform how organizations approach data utilization. By fostering a culture of trust and responsibility in data handling, enterprises can continue to innovate and improve their services while respecting user privacy. This project aims to provide a comprehensive understanding of how privacy-preserving methods can be integrated into the model training process, ensuring that businesses can effectively navigate the complexities of data privacy in an increasingly interconnected world. Ultimately, this research underscores the importance of balancing technological advancement with ethical considerations, paving the way for a future where data privacy and enterprise success coexist harmoniously.
Downloads
References
Agrawal, R., & Srikant, R. (2000, May). Privacy-preserving data mining.
In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 439-450).
Li, X. B., & Sarkar, S. (2014). Digression and value concatenation to enable privacy-preserving regression. MIS quarterly: management information systems, 38(3), 679.
Evfimievski, A., Srikant, R., Agrawal, R., & Gehrke, J. (2002, July). Privacy preserving mining of association rules. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 217-228).
Fung, B. C., Wang, K., Chen, R., & Yu, P. S. (2010). Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys (Csur), 42(4), 1-53.
Wang, C., Chow, S. S., Wang, Q., Ren, K., & Lou, W. (2011). Privacy-preserving public auditing for secure cloud storage. IEEE transactions on computers, 62(2), 362-375.
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data: privacy and data mining. Ieee Access, 2, 1149-1176.
Lindell, & Pinkas. (2002). Privacy preserving data mining. Journal of cryptology, 15, 177-206.
Naor, M., Pinkas, B., & Sumner, R. (1999, November). Privacy preserving auctions and mechanism design. In Proceedings of the 1st ACM Conference on Electronic Commerce (pp. 129-139).
Xiao, Z., & Xiao, Y. (2012). Security and privacy in cloud computing. IEEE communications surveys & tutorials, 15(2), 843-859.
Ziegeldorf, J. H., Morchon, O. G., & Wehrle, K. (2014). Privacy in the Internet of Things: threats and challenges. Security and Communication Networks, 7(12), 2728-2742.
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016). Stealing machine learning models via prediction {APIs}. In 25th USENIX security symposium (USENIX Security 16) (pp. 601-618).
Islam, S., Keung, J., Lee, K., & Liu, A. (2012). Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems, 28(1), 155-162.
Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of network and computer applications, 34(1), 1-11.
Roman, R., Zhou, J., & Lopez, J. (2013). On the features and challenges of security and privacy in distributed internet of things. Computer networks, 57(10), 2266-2279.
Fernández-Alemán, J. L., Señor, I. C., Lozoya, P. Á. O., & Toval, A. (2013). Security and privacy in electronic health records: A systematic literature review. Journal of biomedical informatics, 46(3), 541-562.
Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).
Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
