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20232026
most citedTo Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods

19 citations · 45 across the 13 of their papers we have counts for

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Showing cs.CRShow all

5 papers · 1 filter

cs.CR2026

SoK: From Generation to Consumption of Privacy Documents in Software Systems

Shidong Pan, Clark LaChance, Zhen Tao +1

Privacy documents (e.g., privacy policies) are a central mechanism through which digital services disclose data practices and seek user consent. Over the past decades, research on…

cs.CR2024★ 3 cited

A Solution toward Transparent and Practical AI Regulation: Privacy Nutrition Labels for Open-source Generative AI-based Applications

Meixue Si, Shidong Pan, Dianshu Liao +4

The rapid development and widespread adoption of Generative Artificial Intelligence-based (GAI) applications have greatly enriched our daily lives, benefiting people by enhancing c…

cs.CR2024★ 1 cited

{A New Hope}: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation

Shidong Pan, Zhen Tao, Thong Hoang +7

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, th…

cs.CR2023★ 3 cited

SeePrivacy: Automated Contextual Privacy Policy Generation for Mobile Applications

Shidong Pan, Zhen Tao, Thong Hoang +5

Privacy policies have become the most critical approach to safeguarding individuals' privacy and digital security. To enhance their presentation and readability, researchers propos…

cs.CR2023★ 6 cited

Toward the Cure of Privacy Policy Reading Phobia: Automated Generation of Privacy Nutrition Labels From Privacy Policies

Shidong Pan, Thong Hoang, Dawen Zhang +4

Software applications have become an omnipresent part of modern society. The consequent privacy policies of these applications play a significant role in informing customers how th…