This study empirically analyzed 41 mobile gaming apps, finding that while device ID disclosures were relatively consistent, location and personal information disclosures showed significant mismatches…

The study surveyed Android developers to assess their willingness to adopt changes that mitigate device fingerprinting risks, finding that developers overwhelmingly support privacy protections even wi…

This study empirically analyzed 1,000 Android apps, finding that privacy policies are often vague and frequently fail to align with the actual sensitive data logged by the applications.

The paper introduces MyPhoneBench, a new framework that demonstrates that current phone-use agents often fail to respect user privacy, even when successfully completing simple tasks, primarily due to…

This study investigates user perceptions of privacy risks associated with GenAI smartphones, finding that users express heightened concerns across the entire data lifecycle and suggest comprehensive,…

PolicyGapper is an LLM-based tool that automatically detects inconsistencies and omissions between a mobile app's Google Play Data Safety Section and its official Privacy Policy, identifying thousands…

The paper empirically compares the security and privacy implementation characteristics of major Android messaging apps (Meta Messenger, Signal, and Telegram) using static and dynamic analysis, finding…

The paper proposes a privacy-by-design pipeline for Android malware detection that achieves strong performance by avoiding the collection of sensitive user data entirely.

PrivacyAssist is a multi-agent LLM framework that detects inconsistencies between user-granted app permissions and the app's actual data collection practices, finding that most apps are not fully tran…

The paper analyzed 25 popular mental health apps and found significant privacy gaps, revealing that most apps fail to disclose embedded trackers and dangerous permissions, undermining informed user co…

The paper analyzes Android's permission system and finds that two legacy mechanisms—permission groups and normal-level custom permissions—allow apps to silently gain excessive permissions and expose s…

The paper investigates how users perceive and utilize security and privacy transparency in consumer-facing generative AI, finding that users rely on proxies like popularity and require actionable, tru…

This study proposes a negotiation framework, using composite indices (RBTI and CATI), to explain how youth navigate competing privacy pressures when using smart voice assistants, finding that high usa…

The paper empirically investigates the lead marketing ecosystem, revealing a highly non-compliant system that aggressively collects, shares, and monetizes sensitive personal data through deceptive bro…

The paper proposes Trajectory Induced Preference Optimization (TIPO) to improve mobile GUI agent personalization by explicitly modeling and optimizing for privacy-related behavioral differences in exe…

This paper empirically characterizes the clandestine third-party iOS app stores in Iran, revealing a complex ecosystem driven by sanctions and censorship that facilitates piracy, unauthorized monetiza…

The paper introduces the PrivacyIceberg framework to systematically categorize and empirically demonstrate the high risk of automated, deep personal profiling using LLM agents, revealing a significant…

The paper proposes and evaluates DePRa, a system that democratizes privacy assessment by making everyday users active evaluators of mobile app data access, showing its potential to complement expert a…

MaskClaw is an edge-side privacy arbitrator that protects sensitive data in GUI agent screenshots by combining local visual evidence, task-specific policies, and a skill-evolution mechanism.

The paper reverse-engineers Apple's Private Cloud Compute (PCC) implementation to independently benchmark its model and evaluate its privacy claims, addressing the lack of transparency in Apple's syst…