Legal Ease (Privacy Policy Simplification Web Extension): Implications of Disclosing Personal Information across Various Platforms
P. V. Siva Kumar *
Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, 500090, India.
D. Nisritha
Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, 500090, India.
M. Sreeja
Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, 500090, India.
V. Jahnavi
Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, 500090, India.
R. N. S. Keerthana
Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, 500090, India.
S. Shalini
Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology, Hyderabad, Telangana, 500090, India.
*Author to whom correspondence should be addressed.
Abstract
Legal Ease is a privacy-first web extension that demystifies the dense, often confusing language of online privacy policies. Designed for users who lack the time or legal expertise to decipher these documents, Legal Ease uses cutting-edge Natural Language Processing (NLP) and Machine Learning (ML) models—including Random Forest, AdaBoost, and XGBoost—to automatically extract, classify, and translate complex legal content into clear, concise, and accessible summaries. The extension highlights essential information such as the types of personal data collected, how that data is used, shared, or stored, and any associated risks or privacy concerns. Unlike conventional solutions, Legal Ease delivers real-time, context-aware summaries tailored to the specific website being visited—all without storing or retaining the original legal documents. This ensures maximum transparency while preserving user privacy at every step.
Keywords: Privacy policies, legal ease, user privacy, Machine Learning (ML) models