Author(s)

Isabella Fernandez¹, Aditya Raghavan^2*

Affiliation(s)

¹Department of Information Systems, Universitat Politècnica de València, Spain.

²Department of Computer Science, Indian Institute of Technology Bombay, India.

Corresponding Author

Aditya Raghavan

ABSTRACT

Recommendation systems have become an integral part of the digital landscape, powering personalized experiences and driving engagement across a wide range of industries. However, traditional recommendation systems face significant challenges, including data privacy concerns, lack of transparency, and susceptibility to manipulation. This paper explores how the integration of blockchain technology can revolutionize the field of recommendation systems, addressing these longstanding issues and unlocking new possibilities for more secure, transparent, and user-centric personalization.

By leveraging the decentralized, immutable, and cryptographically secure nature of blockchain, this paper examines the potential of blockchain-based recommendation systems to enhance data privacy, ensure algorithm transparency, and facilitate user control over personal data. Additionally, the paper delves into the synergies between blockchain and other emerging technologies, such as federated learning and differential privacy, to further strengthen the security and reliability of recommendation systems.

Through a comprehensive analysis of use cases, technical considerations, and implementation challenges, this paper serves as a roadmap for businesses, researchers, and technology professionals seeking to harness the transformative power of blockchain in reinventing the future of personalized recommendations. By embracing this innovative approach, organizations can build trust, empower users, and deliver more effective and ethical recommendation experiences.

KEYWORDS

Blockchain; Privacy; Recommendation systems; Computer systems

CITE THIS PAPER

Isabella Fernandez, Aditya Raghavan. Integrating differential privacy with blockchain for privacy-preserving recommendation systems. World Journal of Information Technology. 2024, 2(3): 27-32. DOI: https://doi.org/10.61784/wjit3010.

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