Privacy and Utility Preserving Sensor-Data Transformations
By Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi. 2019
40 citations recorded by the source corpus.
Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users' devices. These transformations aim at eliminating patterns that can be used for user re-identification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application (or task). We show that, on gesture and activity recognition tasks, we can prevent inference of potentially sensitive activities while keeping the reduction in recognition accuracy of non-sensitive activities to less than 5 percentage points. We also show that we can reduce the accuracy of user re-identification and of the potential inference of gender to the level of a random guess, while keeping the accuracy of activity recognition comparable to that obtained on the original data.
Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Praneeth Vepakomma
- Abhishek Singh
- Ramesh Raskar
- Hadi Esmaeilzadeh
- Fatemehsadat Mirshghallah
- Mohammadkazem Taram
- Otkrist Gupta
- Chaoyang He
In Information Systems
In Sociology and Political Science
- Ramesh Raskar
- Fatemehsadat Mirshghallah
- Hadi Esmaeilzadeh
- Abhishek Singh
- Mohammadkazem Taram
- Praneeth Vepakomma
Citations in the corpus
- Privacy in Deep Learning: A Survey (2020, background): “Privacy-preserving schemes that rely on information-theoretic approaches usually assume a nonsensitive task, the task that the service is supposed to execute and try to degrade any excessive information in the input data that is not needed for the main inference task [142, 145, 143, 29, 31, 146].”