• G. KARTHIKEYAN
• Mrs. K. VIJAYALAKSHMI
• Ms. M. DHANALAKSHMI
• Mrs. N.T. SOWMIYA

Personalized Web Search (PWS) enhances search quality by tailoring results to individual user preferences, but users’ reluctance to share private information limits its adoption. This paper presents UPS, a privacy-aware PWS framework that models user preferences as hierarchical profiles and adaptively generalizes them at runtime based on queries while honoring user-specified privacy requirements. UPS balances personalization utility against privacy risk through two greedy algorithms GreedyDP and GreedyIL for profile generalization. Additionally, an online prediction mechanism determines whether personalizing a query is beneficial. Extensive experiments demonstrate that UPS effectively improves search performance, with GreedyIL outperforming GreedyDP in efficiency, highlighting its potential for privacy-conscious personalized search.

Personalized Web Search (PWS), Privacy Aware Search, User Profile Generalization, Greedy Algorithms, Personalization Utility, Privacy Risk Management, Online Prediction Mechanism

"PRIVACY AWARE PERSONALIZED WEB SEARCH VIA ADAPTIVE PROFILE GENERALIZATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.a498-a502, November-2025, Available :http://www.jetir.org/papers/JETIR2511056.pdf

"PRIVACY AWARE PERSONALIZED WEB SEARCH VIA ADAPTIVE PROFILE GENERALIZATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppa498-a502, November-2025, Available at : http://www.jetir.org/papers/JETIR2511056.pdf