International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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Secure Multi-Party Computation for Collaborative Data Analysis Oliv J Patel1, Dhruvil R Patel2, Riddhi A Mehta3 1,2 Student, 3 Assistant Professor Computer Science & Engineering Parul Institute of Technology, Parul University Vadodara, Gujarat, India ---------------------------------------------------------------------***--------------------------------------------------------------------analyzing group data at SMPC are also noted. Scalability Abstract - A powerful encryption mechanism called Secure
issues, performance optimization, dynamic party management and fighting malicious adversaries are some of them. The report suggests potential research opportunities to address these issues and increase the use of SMPC in the real world. The study analyses case studies and real-world applications to ensure the effectiveness of SMPC. It tells about the effective use of SMPC in many fields, demonstrates its applicability and highlights the implications of these experiences. The need for secure and privacy-friendly data analysis methods has grown in the era of big data and collaborative research. Traditional data analysis techniques fail to provide adequate protection against privacy breaches and unauthorized access due to the collection and exchange of sensitive data between multiple parties. Secure MultiParty Computation (MPC), developed to address these problems, has proven to be an effective solution because many parties can collaboratively compute functions based on their private data without revealing background information.
Multi-Party Computation (SMPC) was developed to allow many participants to collaborate and perform data analysis tasks while maintaining the privacy and secrecy of their personal information. In many fields, such as health care, finance, and social sciences, where multiple stakeholders need to exchange and evaluate sensitive information without disclosing it to others, collaborative data analysis is becoming more common. This study provides an in-depth study of SMPC for group data analysis. The main goal is to provide a comprehensive understanding of the leading ideas, protocols, and applications of SMPC, while highlighting the benefits and challenges it brings to promote secure collaboration between different data owners. In summary, this study provides a comprehensive and up-to-date study on secure multiparty computing in collaborative data review. It provides a comprehensive overview of SMPC implementation issues and the underlying ideas, protocols, and applications. The article is intended to be a useful resource for researchers, practitioners, and policy makers interested in using SMPC to facilitate group data analysis while protecting confidentiality and privacy.
Secure MPC, also known as Secure Multiparty Computing, is a cryptographic system that allows multiple parties to perform calculations on their shared data while protecting the privacy and confidentiality of individual inputs. MPC enables decentralized computing where each partner retains ownership of its data, unlike traditional systems that require data sharing or outsourcing of computations to a central server. The primary purpose of Secure MPC is to enable collaborative data analysis while protecting confidentiality and privacy. MPC guarantees that each partner's inputs and intermediate calculations remain private during the analysis process using various encryption methods, including homomorphic encryption, secret sharing, and security protocols. It allows companies, researchers, and individuals to collaborate and analyses integrated datasets without having to reveal or disclose their sensitive data. The concept of "privacy by design" is one of the core ideas of Secure MPC. This demonstrates that security and privacy considerations are considered during the development and use of computing protocols. This ensures that privacy is preserved by default and removes the need for additional layers of security that can be vulnerable to errors or mistakes. Secure MPC implements privacy through carefully designed encryption algorithm selection, secure key management, and thorough protocol testing and auditing. Secure MPC has
1.INTRODUCTION The paper first introduces the basic ideas of SMPC, such as security function evaluation, secret sharing, and cryptographic primitives. It looks at how these ideas could be used to facilitate collaborative analysis without revealing private information. Yao's Adversary Chains, Secure Multiparty Computation via Boolean Chains (SMC-BC), and Fully Homomorphic Encryption (FHE) are just a few of the SMPC protocols that are explored in depth and their advantages and disadvantages in various situations. In addition, the research examines the precise uses of SMPC in the analysis of group data. It looks at scenarios where multiple hospitals can collaborate to examine patient data for medical research while protecting patient privacy. In addition, it explores financial scenarios where multiple agencies could work together to identify money laundering trends without revealing specific client activities. The threat model, assumptions and level of protection offered by various protocols are all discussed in detail in relation to SMPC security features. The study also discusses the tradeoffs between privacy and efficiency and highlights the computational and communication costs associated with SMPC. Difficulties and unresolved research issues in
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