Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems

Published in ISCA, 2018

Full Paper | bibtex | Plain Text

@inproceedings{10.1145/3373376.3378462,
author = {Sanchez Vicarte, Jose Rodrigo and Schreiber, Benjamin and Paccagnella, Riccardo and Fletcher, Christopher W.},
title = {Game of Threads: Enabling Asynchronous Poisoning Attacks},
year = {2020},
isbn = {9781450371025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3373376.3378462},
doi = {10.1145/3373376.3378462},
booktitle = {Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems},
pages = {35–52},
numpages = {18},
keywords = {adversarial machine learning, trusted execution environment, asynchronous stochastic gradient descent},
location = {Lausanne, Switzerland},
series = {ASPLOS ’20}
}
Jose Rodrigo Sanchez Vicarte, Benjamin Schreiber, Riccardo Paccagnella, and Christopher W. Fletcher. 2020. Game of Threads: Enabling Asynchronous Poisoning Attacks. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’20). Association for Computing Machinery, New York, NY, USA, 35–52. DOI:https://doi.org/10.1145/3373376.3378462
@inproceedings{inproceedings,
author = {Mahmoud, Abdulrahman and Aggarwal, Neeraj and Nobbe, Alex and Vicarte, Jose and Adve, Sarita and Fletcher, Christopher and Frosio, Iuri and Hari, Siva},
year = {2020},
month = {06},
pages = {25-31},
title = {PyTorchFI: A Runtime Perturbation Tool for DNNs},
doi = {10.1109/DSN-W50199.2020.00014}
}
Mahmoud, Abdulrahman & Aggarwal, Neeraj & Nobbe, Alex & Vicarte, Jose & Adve, Sarita & Fletcher, Christopher & Frosio, Iuri & Hari, Siva. (2020). PyTorchFI: A Runtime Perturbation Tool for DNNs.
@inproceedings{10.1109/ISCA.2018.00053,
author = {Choi, Woo-Seok and Tomei, Matthew and Vicarte, Jose Rodrigo Sanchez and Hanumolu, Pavan Kumar and Kumar, Rakesh},
title = {Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems},
year = {2018},
isbn = {9781538659847},
publisher = {IEEE Press},
url = {https://doi.org/10.1109/ISCA.2018.00053},
doi = {10.1109/ISCA.2018.00053},
booktitle = {Proceedings of the 45th Annual International Symposium on Computer Architecture},
pages = {561–574},
numpages = {14},
keywords = {microcontrollers, randomized response, IoT, low-power systems, RAPPOR, differential privacy},
location = {Los Angeles, California},
series = {ISCA ’18}
}
Woo-Seok Choi, Matthew Tomei, Jose Rodrigo Sanchez Vicarte, Pavan Kumar Hanumolu, and Rakesh Kumar. 2018. Guaranteeing local differential privacy on ultra-low-power systems. In Proceedings of the 45th Annual International Symposium on Computer Architecture (ISCA ’18). IEEE Press, 561–574. DOI:https://doi.org/10.1109/ISCA.2018.00053

Sensors in mobile devices and IoT systems increasingly generate data that may contain private information of individuals. Generally, users of such systems are willing to share their data for public and personal benefit as long as their private information is not revealed. A fundamental challenge lies in designing systems and data processing techniques for obtaining meaningful information from sensor data, while maintaining the privacy of the data and individuals. In this work, we explore the feasibility of providing local differential privacy on ultra-low-power systems that power many sensor and IoT applications. We show that low resolution and fixed point nature of ultra-low-power implementations prevent privacy guarantees from being provided due to low quality noising. We present techniques, resampling and thresholding, to overcome this limitation. The techniques, along with a privacy budget control algorithm, are implemented in hardware to provide privacy guarantees with high integrity. We show that our hardware implementation, DP-Box, has low overhead and provides high utility, while guaranteeing local differential privacy, for a range of sensor/IoT benchmarks.