Secure Computation Techniques for Privacy-Preserving Data Processing Systems
Abstract
Privacy preservation in data processing has become a paramount concern as data collection and analytics proliferate across industries and applications. Traditional data processing methods often require raw data exposure, creating risks of unauthorized access, data leaks, and compliance violations. Secure computation techniques aim to enable collaborative data processing while keeping sensitive information confidential. These techniques include cryptographic approaches such as secure multi-party computation (SMPC), homomorphic encryption (HE), private set intersection (PSI), garbled circuits, and trusted execution environments (TEEs). This paper explores the principles and applications of secure computation techniques within privacy-preserving data processing systems. It examines the theoretical foundations, system architectures, and performance considerations that shape their integration into real-world workflows. A comprehensive literature review traces the evolution of secure computation from early cryptographic protocols to contemporary frameworks that support scalable, distributed computation on encrypted or otherwise protected data. A detailed research methodology is proposed for evaluating and benchmarking secure computation techniques across metrics such as security guarantees, computational overhead, communication cost, and practical deployability. The paper further analyzes advantages and disadvantages, supported by results and discussion from both experimental studies and case applications. The study concludes by highlighting key insights into the practical implications of privacy-preserving computation and outlines future research directions necessary to overcome current limitations and enable broader adoption in data-driven domains
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 2 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
11708-11716 |
Published |
March 8, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Nidhi Kavita Bhatnagar (%2023). Secure Computation Techniques for Privacy-Preserving Data Processing Systems. International Journal of Science, Research and Technology , Vol. 7 No. 2 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 11708-11716. https://doi.org/10.15662/IJSRAT.2024.0702001 |
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