Robustness Analysis of a Novel Model-Based Recommendation Algorithms in Privacy Environment
İhsan Güneş · KSII Transactions on Internet and Information Systems · 2024
The concept of privacy-preserving collaborative filtering (PPCF) has been gaining significant attention.Due to the fact that model-based recommendation methods with privacy are more efficient online, privacy-preserving memory-based scheme should be avoided in favor of model-based recommendation methods with privacy.Several studies in the current literature have examined ant colony clustering algorithms that are based on non-privacy collaborative filtering schemes.Nevertheless, the literature does not contain any studies that consider privacy in the context of ant colony clustering-based CF schema.This study employed the ant colony clustering model-based PPCF scheme.Attacks like shilling or profile injection could potentially be successful against privacy-preserving model-based collaborative filtering techniques.Afterwards, the scheme's robustness was assessed by conducting a shilling attack using six different attack models.We utilize masked data-based profile injection attacks against a privacy-preserving ant colony clustering-based prediction algorithm.Subsequently, we conduct extensive experiments utilizing authentic data to assess its robustness against profile injection attacks.In addition, we evaluate the resilience of the ant colony clustering model-based PPCF against shilling attacks by comparing it to established PPCF memory and model-based prediction techniques.The empirical findings indicate that push attack models exerted a substantial influence on the predictions, whereas nuke attack models demonstrated limited efficacy.