Andrea Cavallaro
ABSTRACT
We discuss the design and evaluation of machine learning algorithms that provide users with more control on the multimedia information they share. We introduce privacy threats for multimedia data and key features of privacy protection. We cover privacy threats and mitigating actions for images, videos, and motion-sensor data from mobile and wearable devices, and their protection from unwanted, automatic inferences. The tutorial offers theoretical explanations followed by examples with software developed by the presenters and distributed as open source.
- M.S. Cross, A. Cavallaro. 2020. Privacy as a feature for body worn cameras. Signal Processing Magazine, 37 (4), doi: 10.1109/MSP.2020.2989686.Google Scholar
- A.S. Shamsabadi, R.S. Matilla, A. Cavallaro. 2020. ColorFool: Semantic Adversarial Colorization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, USA.Google ScholarCross Ref
- G. Abebe Tadesse, O. Bent, L. Marcenaro, K. Weldemariam, A. Cavallaro. 2020. Privacy-aware human activity recognition from a wearable camera. Signal Processing Magazine, 37 (3), doi: 10.1109/MSP.2020.2976158.Google Scholar
- A.S. Shamsabadi, C. Oh, A. Cavallaro. 2020. EdgeFool: An Adversarial Image Enhancement Filter. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.Google ScholarCross Ref
- R. S. Matilla, C. Y. Li, A. S. Shamsabadi, R. Mazzon, A. Cavallaro. 2020. Exploiting vulnerabilities of deep neural networks for privacy protection. IEEE Transactions on Multimedia, 22 (7), doi: 10.1109/TMM.2020.2987694.Google Scholar
- A. S. Shamsabadi, A. Gascon, H. Haddadi, A. Cavallaro. 2020. PrivEdge: From Local to Distributed Private Training and Prediction. IEEE Transactions on Information Forensics and Security, 15, doi: 10.1109/TIFS.2020.2988132.Google Scholar
- S. A. Osia, A. S. Shamsabadi, S. Sajadmanesh, A. Taheri, K. Katevas, H. R. Rabiee, N. D. Lane, H. Haddadi. 2020. A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics. IEEE Internet of Things Journal.Google Scholar
- M. Malekzadeh, R. G. Clegg, A. Cavallaro, H. Haddadi. 2020. Privacy and Utility Preserving Sensor Data Transformations. Pervasive and Mobile Computing, Volume 63, Article 101132.Google ScholarCross Ref
- M. Malekzadeh, D. Athanasakis, H. Haddadi, B. Livshits. 2020. Privacy-Preserving Bandits. In Proceedings of the Conference on Machine Learning and Systems (MLSys), Huston, Texas, USA.Google Scholar
- O. Sarwar, B. Rinner, A. Cavallaro. 2019. A privacy-preserving filter for oblique face images based on adaptive hopping Gaussian mixtures. IEEE Access, 7, doi: 10.1109/ACCESS.2019.2944861.Google Scholar
- C. Y. Li, A. S. Shamsabadi, R. S. Matilla, R. Mazzon, A. Cavallaro. 2019. Scene Privacy Protection. In Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.Google ScholarCross Ref
- M. Malekzadeh, R. G. Clegg, A. Cavallaro, H. Haddadi. 2019. Mobile Sensor Data Anonymization. In Proc. of the IEEE/ACM International Conference on Internet of Things Design and Implementation (IoTDI), Montreal, Canada.Google ScholarDigital Library
- A. S. Shamsabadi, H. Haddadi, A. Cavallaro. 2018. Distributed One-Class Learning. In Proceedings of the IEEE International Conference on Image Processing (ICIP), Athens, Greece.Google ScholarCross Ref
- S. A. Osia, A. Taheri, A. S. Shamsabadi, K. Katevas, H. Haddadi, H. R. Rabiee. 2018. Deep Private-Feature Extraction. IEEE Transactions on Knowledge and Data Engineering.Google Scholar
- S. A. Osia, A. S. Shamsabadi, A. Taheri, H. R. Rabiee, H. Haddadi. 2018. Private and Scalable Personal Data Analytics using Hybrid Edge-Cloud Deep Learning. IEEE Computer, Special Issue on Mobile and Embedded Deep Learning.Google Scholar
- M. Malekzadeh, R. G. Clegg, A. Cavallaro, H. Haddadi. 2018. Protecting Sensory Data Against Sensitive Inferences. In Proc. of the 1st ACM Workshop on Privacy by Design in Distributed Systems (W-P2DS), Porto, Portugal.Google ScholarDigital Library
- M. Malekzadeh, R. G. Clegg, H. Haddadi. 2018. Replacement Autoencoder: A Privacy-Preserving Algorithm for Sensory Data Analysis. In Proceedings of the IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI), Orlando, Florida, USA.Google ScholarCross Ref
- O. Sarwar, A. Cavallaro, B. Rinner. 2018. Temporally smooth privacy protected airborne videos. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.Google ScholarCross Ref
Index Terms
- Deep Learning for Privacy in Multimedia
Recommendations
De-identification for privacy protection in multimedia content
Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud ...
Controlling and disclosing your personal information
As organizations come to rely on the collection and use of personal information in order to complete the transactions and providing good services to their users, more and more user personal information is being shared with web service providers leading ...
Privacy implications of context-aware services
COMSWARE '09: Proceedings of the Fourth International ICST Conference on COMmunication System softWAre and middlewaREPersonal information handling in current and anticipated context use cases is worrisome. This notwithstanding, contextual services are rapidly proliferating into most niches of society, catalysed by technological advancements in sensing, ubiquity and ...
Comments