no code implementations • 25 Apr 2020 • Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh
In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues.
no code implementations • 26 Mar 2020 • Fatemehsadat Mireshghallah, Mohammadkazem Taram, Ali Jalali, Ahmed Taha Elthakeb, Dean Tullsen, Hadi Esmaeilzadeh
We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider.
3 code implementations • 26 May 2019 • Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani, Dean Tullsen, Hadi Esmaeilzadeh
To address this challenge, this paper devises Shredder, an end-to-end framework, that, without altering the topology or the weights of a pre-trained network, learns additive noise distributions that significantly reduce the information content of communicated data while maintaining the inference accuracy.