A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
Falguni Roy, Xiaofeng Ding, Kiam Choo · 2026 · 2 citationsRead the paper
Recommender systems personalize digital experiences across e-commerce, streaming, and social media, yet they face linked challenges in fairness, bias, threats, and privacy. Bias can lead to unequal treatment, so fairness seeks equitable recommendations for all stakeholders. At the same time, adversarial attacks can erode reliability, and extensive use of personal data creates privacy risks that demand robust safeguards. This study surveys these challenges, shows how algorithmic choices can unintentionally reinforce disparities, and reviews strategies for fair recommendation and privacy-preserving learning. We frame these aspects with a unified Privacy–Accuracy–Fairness plus Threat framework that spans data retrieval, ranking and serving, and we advocate standardized evaluation on fixed candidate item pools to isolate tradeoffs between quality and responsibility metrics. By integrating these dimensions, we synthesize current limitations and outline future directions to improve threat resilience, fairness and privacy. We also situate the discussion in ethical and regulatory contexts and translate those principles into practical controls that foster trustworthy systems for diverse populations.
What the full text shows
Theory or simulation- Cryptography-based methods (e.g., HE, FHE) provide strong security and data privacy but suffer from high computational and communication overhead. (Large-scale datasets with millions of users/items)
- Non-cryptographic methods (e.g., Differential Privacy, Obfuscation) offer lower computational complexity but introduce a privacy-utility trade-off. (Real-time applications and large-scale datasets)
- Combining k-anonymity, l-diversity, and t-closeness strengthens anonymization and reinforces privacy preservation compared to using l-diversity alone. (User attribute anonymization)
- Decentralized architectures provide better privacy than centralized ones as users actively manage their own data. (Semi-trustworthy or non-trustworthy server environments)
- Setup
- Review of multiple datasets including MovieLens (ML@100k, ML@1M, ML@20M), Netflix, Amazon, and synthetic datasets across domains like Health, POI, and Social Networks.
- How
- The paper provides a comprehensive survey and taxonomy of Privacy-Preserving Recommender Systems (PPRS), reviewing cryptographic (HE, FHE, ABE) and non-cryptographic (DP, k-anonymity, perturbation) techniques. It proposes a unified Privacy–Accuracy–Fairness plus Threat framework to evaluate trade-offs.
- Limitations
- Cryptography methods scale poorly with large data sizes, leading to inefficiencies. · Non-cryptographic methods risk privacy breaches if noise calibration is insufficient. · Secret sharing requires constant online availability of all clients. · Inherent trade-off between privacy and recommendation accuracy (utility).
Summarized by Gemma 4 from the open-access full text · 8 Oct 2026. Read it