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Stream/Area:
-
Subject:
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Paper ID:
IJSREM62407
Volume:
10 |
5 |
May 2026
Pages:
1--
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science & Engineering
MIT ADT UNIVERSITY
Department of Computer Science & Engineering
MIT ADT UNIVERSITY
Department of Computer Science & Engineering
MIT ADT UNIVERSITY
Department of Computer Science & Engineering
MIT ADT UNIVERSITY
Most people have never read a privacy policy - not because they don't care, but because these documents were never really written for them. Estimates suggest that keeping up with the privacy policies a single user encounters online would consume over 180 hours a year, which means agreeing to them has become a reflex rather than a decision. The tools that exist to help haven't changed this much - they flag known bad actors but stay silent about everything else. Sebastianzimmeck PrivAware is a browser extension built around the idea that privacy protection should happen automatically, in the background, without asking users to become experts. It watches what a page is actually doing - where it's sending data, what it's tracking, whether its stated privacy policy matches its behavior - and surfaces a simple risk score with a plain-language explanation. No setup, no manual review, no legal literacy required. This paper describes how the system was designed, how its underlying models were trained, and what it reveals about the gap between what websites claim and what they do.
KEYWORDS
The ever-growing online threat landscape means that current security systems that rely on fixed blocklists are not adequate in combating modern phishing attacks and shady privacy practices. PrivAware addresses this by integrating real-time anti-phishing with privacy policy evaluation into an easy-to-understand Chrome extension. Using transformer-based natural language processing (NLP) models with DistilBERT and the API system it enables, the product provides real-time, accurate, and interpretable risk scores for any web page. PrivAware provides a single risk score that combines phishing, privacy policy and tracker information, allowing users to assess and mitigate cyber risks. Evaluation results demonstrate that PrivAware has high precision, recall and accuracy, indicating it effectively identifies malicious links and potentially deceptive privacy statements. The system's integration with a browser extension makes it easy to use in real-time for non-technical users. With future technological improvements in explainable AI, multi-language support and user preferences, PrivAware may become a user's personalised digital privacy coach. In conclusion, the system is a step in the direction of proactive, intelligent and user-friendly web security, bringing cybersecurity to real users.
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Publication Date
5/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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