Privacy Preserving Recommender Systems Balancing Personalization with Privacy

• URL: http://arxiv.org/abs/2607.13328v1
• Date: Tue, 14 Jul 2026 23:21:21 GMT
• Title: Privacy Preserving Recommender Systems Balancing Personalization with Privacy
• Abstract summary: This work presents a privacy-preserving recommendation framework that combines federated learning, differential privacy, cohort-level modeling, and privacy-aware intelligent agents.<n>Experiments were conducted on synthetic retail datasets that emulate customer clickstream and purchase behavior.<n>Results show that the proposed framework maintains competitive recommendation quality at moderate privacy budgets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 recommendation framework that combines federated learning, differential privacy, cohort-level modeling, and privacy-aware intelligent agents. The framework keeps raw user data decentralized while introducing mathematically bounded noise to model updates. Experiments were conducted on synthetic retail datasets that emulate customer clickstream and purchase behavior. Recommendation quality was evaluated using Click-Through Rate (CTR), Precision@K, Recall@K, and Normalized Discounted Cumulative Gain (NDCG@K) across multiple differential privacy budgets. We evaluate matrix factorization, neural collaborative filtering, and GRU4Rec under varying privacy constraints and analyze the trade-off between privacy and utility. An interactive Streamlit dashboard was developed to visualize recommendation performance, ranking stability, privacy-utility trade-offs, and fairness metrics. Results show that the proposed framework maintains competitive recommendation quality at moderate privacy budgets (approximately $ε\approx 5$), demonstrating that strong privacy guarantees can be achieved with limited impact on recommendation effectiveness. This work provides a practical framework for deploying privacy-preserving recommendation systems that balance personalization, regulatory compliance, and business objectives, offering a scalable approach for next-generation AI-driven retail platforms.

Related papers

• TADP-RME: A Trust-Adaptive Differential Privacy Framework for Enhancing Reliability of Data-Driven Systems [0.5926203312586108]
  We propose TADP-RME (Trust-Adaptive Differential Privacy with Reverse Manifold Embedding), a framework that enhances reliability under varying levels of user trust.<n>We show improved privacy-utility trade-offs, reducing attack success rates by up to 3.1 percent without significant utility degradation.
  arXiv  Detail & Related papers  (2026-04-09T11:32:57Z)
• A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy [10.908551029176822]
  The framework combines distributed feature extraction, dynamic privacy budget allocation, and robust model aggregation to balance model accuracy, communication overhead, and privacy protection.<n> Experimental results demonstrate that the framework achieves dual optimization of recommendation accuracy and system efficiency while ensuring privacy.
  arXiv  Detail & Related papers  (2025-07-16T10:07:19Z)
• Personalized Language Models via Privacy-Preserving Evolutionary Model Merging [53.97323896430374]
  Personalization in language models aims to tailor model behavior to individual users or user groups.<n>We propose Privacy-Preserving Model Merging via Evolutionary Algorithms (PriME)<n>PriME employs gradient-free methods to directly optimize utility while reducing privacy risks.<n>Experiments on the LaMP benchmark show that PriME consistently outperforms a range of baselines, achieving up to a 45% improvement in task performance.
  arXiv  Detail & Related papers  (2025-03-23T09:46:07Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• Privacy-Preserving Dynamic Assortment Selection [4.399892832075127]
  This paper presents a novel framework for privacy-preserving dynamic assortment selection using the multinomial logit (MNL) bandits model. Our approach integrates noise into user utility estimates to balance between exploration and exploitation while ensuring robust privacy protection.
  arXiv  Detail & Related papers  (2024-10-29T19:28:01Z)
• Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy [55.357715095623554]
  Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. We propose a Bayesian framework, Bayesian Coordinate Differential Privacy (BCDP), that enables feature-specific privacy quantification.
  arXiv  Detail & Related papers  (2024-10-24T03:39:55Z)
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack [55.24441467292359]
  We propose a user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent.
  arXiv  Detail & Related papers  (2023-12-23T09:44:57Z)
• A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
  We propose a new differential privacy paradigm called estimate-verify-release (EVR) EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
  arXiv  Detail & Related papers  (2023-04-17T00:38:01Z)
• Privacy-Preserving Matrix Factorization for Recommendation Systems using Gaussian Mechanism [2.84279467589473]
  We propose a privacy-preserving recommendation system based on the differential privacy framework and matrix factorization. As differential privacy is a powerful and robust mathematical framework for designing privacy-preserving machine learning algorithms, it is possible to prevent adversaries from extracting sensitive user information.
  arXiv  Detail & Related papers  (2023-04-11T13:50:39Z)
• Decentralized Matrix Factorization with Heterogeneous Differential Privacy [2.4743508801114444]
  We propose a novel Heterogeneous Differentially Private Matrix Factorization algorithm (denoted as HDPMF) for untrusted recommender. Our framework uses modified stretching mechanism with an innovative rescaling scheme to achieve better trade off between privacy and accuracy.
  arXiv  Detail & Related papers  (2022-12-01T06:48:18Z)
• Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
  Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
  arXiv  Detail & Related papers  (2022-05-08T14:38:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.