Privacy preserving release of mobile sensor data

Rahat Masood, Wing Yan Cheng, Dinusha Vatsalan, Deepak Mishra, Hassan Jameel Asghar, Dali Kaafar

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

Sensors embedded in mobile smart devices can monitor users' activity with high accuracy to provide a variety of services to end-users ranging from precise geolocation, health monitoring, and handwritten word recognition. However, this involves the risk of accessing and potentially disclosing sensitive information of individuals to the apps that may lead to privacy breaches. In this paper, we aim to minimize privacy leakages that may lead to user identification on mobile devices through user tracking and distinguishability while preserving the functionality of apps and services. We propose a privacy-preserving mechanism that effectively handles the sensor data fluctuations (e.g., inconsistent sensor readings while walking, sitting, and running at different times) by formulating the data as time-series modeling and forecasting. The proposed mechanism uses correlated noise-series against noise filtering attacks from an adversary, which aims to filter out the noise from the perturbed data to re-identify the original data. Unlike existing solutions, our mechanism keeps running in isolation without the interaction of a user or a service provider. We perform rigorous experiments on three benchmark datasets and show that our proposed mechanism limits user tracking and distinguishability threats to a significant extent compared to the original data while maintaining a reasonable level of utility of functionalities. In general, we show that our obfuscation mechanism reduces the user trackability threat by 60% across all the datasets while maintaining the utility loss below 0.3 Mean Absolute Error (MAE). More specifically, we observe that 80% of users achieve a 100% untrackability rate in the Swipes dataset across all noise scales. In the handwriting dataset, distinguishability is 17% for 60% of the users. Overall, our mechanism provides a utility error (MAE) of only 0.12 for 60% of users, and this increases to 0.2 for 100% users when correction thresholds are altered.
Original languageEnglish
Title of host publicationARES '24
Subtitle of host publicationproceedings of the 19th International Conference on Availability, Reliability and Security
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Number of pages13
ISBN (Electronic)9798400717185
DOIs
Publication statusPublished - 2024
EventInternational Conference on Availability, Reliability and Security (19th : 2024) - Vienna, Austria
Duration: 30 Jul 20242 Aug 2024
Conference number: 19th

Conference

ConferenceInternational Conference on Availability, Reliability and Security (19th : 2024)
Abbreviated titleARES '24
Country/TerritoryAustria
CityVienna
Period30/07/242/08/24

Keywords

  • Sensor Data
  • Data Obfuscation
  • Time-Series Analysis
  • User Tracking
  • Distinguishability
  • Noise Filtering Attack

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