PMDP: A Secure Multiparty Computation Framework for Maintaining Multiparty Data Privacy in Cloud Computing

Abstract

Ensuring the privacy and security of sensitive information is critical in the age of cloud computing, as data sharing and collaboration grow more common. Secure Multiparty Computation (MPC) appears as a viable cryptographic solution that allows several parties to collaborate and compute functions over their inputs while maintaining data confidentiality. To address the need for multiparty data privacy protection in cloud computing scenarios, the Privacy-preserving Multiparty Data Privacy (PMDP) framework is introduced. PMDP uses advanced cryptography methods and privacy-preserving mechanisms to protect sensitive data from semi-malicious adversaries. The framework takes advantage of the NTRU encryption scheme's ring structure, employing polynomial-based key generation, encryption, and decryption algorithms using a public-private key pair. PMDP also uses the Sample-and-Aggregate algorithm to segment, clip, and aggregate datasets for calculations, as well as Laplace noise to improve security. Furthermore, PMDP incorporates differential privacy concepts to formalize privacy guarantees by restricting the influence of individual data on query results. PMDP was developed collaboratively, drawing on experience from a variety of disciplines such as cloud computing, encryption, and privacy-preserving technologies. Thorough integration and testing processes verify the framework's functionality, durability, and efficacy in real-world cloud computing scenarios. PMDP's performance is evaluated against existing cryptographic approaches, and user feedback and iterative improvement are used to continuously improve the framework's usability and effectiveness. Overall, the systematic methodology used in the design, implementation, and evaluation of PMDP emphasizes its importance as a solid solution for protecting multiparty data privacy.