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PriviSense: A Frida-Based Framework for Multi-Sensor Spoofing on Android

Year: 2026

Type: conference-paper

Abstract: Mobile apps increasingly rely on real-time sensor and system data to adapt their behavior to user context. While emulators and instrumented builds offer partial solutions, they often fail to support reproducible testing of context-sensitive app behavior on physical devices. We present PriviSense, a Frida-based, on-device toolkit for runtime spoofing of sensor and system signals on rooted Android devices. PriviSense can script and inject time-varying sensor streams (accelerometer, gyroscope, step counter) and system values (battery level, system time, device metadata) into unmodified apps, enabling reproducible on-device experiments without emulators or app rewrites. Our demo validates real-time spoofing on a rooted Android device across five representative sensor-visualization apps. By supporting scriptable and reversible manipulation of these values, PriviSense facilitates testing of app logic, uncovering of context-based behaviors, and privacy-focused analysis. To ensure ethical use, the code is shared upon request with verified researchers.

Source: IEEE/ACM International Conference on Software Engineering (ICSE)

DOI: 10.1145/3774748.3787601

Authors: Ibrahim Khalilov, Chaoran Chen, Ziang Xiao, Tianshi Li, Toby Jia-Jun Li, Yaxing Yao

Institutions: Johns Hopkins University, University of Notre Dame, Northeastern University

Topic: Advanced Malware Detection Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Awards: Collaborative Research: SaTC: CORE: Small: Supporting Privacy Negotiation Among Multiple Stakeholders in Smart Environments, Collaborative Research: SaTC: CORE: Small: Empathy-Based Privacy Education and Design through Synthetic Persona Data Generation, Collaborative Research: SaTC: CORE: Small: Empathy-Based Privacy Education and Design through Synthetic Persona Data Generation, Collaborative Research: SHF: Medium: Towards More Human-like AI Models of Source Code, Collaborative Research: SaTC: CORE: Small: Empathy-Based Privacy Education and Design through Synthetic Persona Data Generation, CAREER: Enhancing Privacy Literacy for Elementary School Children via Long-Term Family, School, and Community Partnerships, FW-HTF-RM: Bridging AI Inequality in Digitally-Mediated Gig Work

OpenAlex ID: W7127099780

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