A Serverless Client-Side Privacy Index for Sensitive Data Processing
: The widespread adoption of data-driven systems has intensified concerns regarding the protection of sensitive information and the assessment of privacy risks. Although numerous privacy-preserving techniques and models have been proposed, quantifying and interpreting the level of privacy achieved remains challenging, particularly for non-expert users. This paper introduces the Privacy Index, a unified metric designed to aggregate multiple privacy-related factors into a single, interpretable score. The proposed approach integrates the validation of established privacy models, detection of anonymization techniques, and estimation of re-identification risk under different attacker assumptions. To support practical usage, we develop a serverless, client-side web application that automatically processes structured datasets by classifying attributes and computing the Privacy Index without transmitting data externally. Experimental evaluation demonstrates that the attribute classification component achieves average confidence levels above 88% across multiple datasets, correctly identifying all direct identifiers with high reliability. The system successfully validates privacy models such as k -anonymity and l -diversity and effectively distinguishes between poorly and well-anonymized datasets. The tool is open-source, its code is available at https://github.com/ieeta-mith/DataPrivScore and a demo is available through https://ieeta-mith.github.io/DataPrivScore/.