Client-Based Learning and Zero-Knowledge Proof Implementation in Social Media Recommendation Systems

Abstract

Social media platforms rely heavily on user interaction data to personalize content and advertisements, raising concerns regarding user privacy and data misuse. Although regulations such as the General Data Protection Regulation (GDPR) aim to address these concerns, enforcement remains under the control of the platforms themselves. To address these issues, this paper proposes a privacy-preserving recommender system that minimizes personal preference data exposure while maintaining the potential for equivalent personalization accuracy (including for advertisements) through local data processing, which could access the same amount or more of pure user data than server-side models. This system integrates a lightweight client-based machine learning model to infer user preferences locally, combined with Merkle tree-based Zero-Knowledge Proof (ZKP) scheme to anonymously authenticate user requests. The authors develop a working web app prototype and evaluate performance across a range of user devices. Results show minimal latency for the client-based model (under 5 milliseconds on most devices) and diverse proof generation times, ranging from 2.6 seconds to over 18 seconds, depending on hardware capability. Server-side verification remains consistent and fast under 250 milliseconds. Although proof generation latency remains a bottleneck for real-time applications, optimization strategies such as proof-caching, cross-application preferences synchronization, and native implementation provides a promising path toward privacy-preserving personalization in social media systems.