Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques

¹ Assistant Professor, Department of CSE, Bipin Tripathi Kumaon Institute of Technology Dwarahat, Distt Almora, Uttrakhand, India
² Assistant Professor, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India
³ Assistant Professor, Department of Computer Science and Engineering (AIML), CVR College of Engineering, Hyderabad, Telangana, India
⁴ Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, Chittagong, Bangladesh
⁵ Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
⁶ Assistant Professor, Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

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Abstract

Distributed systems are now an indispensable part of modern computing, and so too must be their security, with privacy-preserving computations being in high demand. Current homomorphic encryption (HE) and secure computation methods suffer from drawbacks like significant computational overhead, scalability issues, network inefficiencies, and non-practical implementations for low-power and edge systems. This challenges the data due to privacy and requires an HE-based secure computation framework as an essential part. The proposed method introduces lightweight Homomorphs Encryption (HE) algorithms with shortened bootstrapping times, adaptive privacy frameworks, and hybrid secure computation techniques joining together multi-party computation (MPC). In addition, the hardware efficiency and network efficiency encryption algorithms have been integrated in our model, securing real-time performance in distributed-systems scenarios. Traditional solutions tend to use standard techniques like basic data wrapping and cryptographic 'rings'; but, due to the design properties required, they end up as lightweight mechanisms, usually not interpretation-at-all capable because of the need for protecting data during processing - leaving these applications hard to use and maintain long-term, or otherwise, limited to cloud computing and federated learning, when individual data types can be worked on within providers like AWS, Azure, etc, etc; or, even, explaining the results with near total indifference to the underlying big data tools, analytics, or neural architectures. The effectiveness of our proposed model is shown through real-world benchmarking and experimental validation, ensuring the capabilities of our approach in securing distributed systems without burdening computational power.