Privacy Preserving Recommender Systems Balancing Personalization with Privacy
Personalized recommendation systems are central to modern e-commerce and retail platforms, but they typically rely on centralized storage of detailed user interaction data, creating significant privacy and regulatory challenges. With increasing requirements from regulations such as GDPR, CCPA, and CPRA, organizations must develop recommendation systems that preserve user privacy without substantially degrading recommendation quality. This work presents and evaluates a privacy-preserving recommen
Record details
Published: 14 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy
Retrieved: 16 July 2026
Related evidence
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How to cite this record
ethics.ai (14 July 2026), “Privacy Preserving Recommender Systems Balancing Personalization with Privacy,” evidence record 10607, https://ethics.ai/record/10607 (originally published by arXiv).
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