| 1. | Kritika Prakash; Fiza Hussain; Praveen Paruchuri; Sujit Gujar: How Private Is Your RL Policy? An Inverse RL Based Analysis Framework. In: 36th AAAI Conference on Artificial Intelligence , AAAI, Forthcoming. (Type: Conference | BibTeX | Tags: differential privacy, inverse reinforcement learning, Reinforcement Learning)@inproceedings{Kritika22,
title = {How Private Is Your RL Policy? An Inverse RL Based Analysis Framework},
author = {Kritika Prakash and Fiza Hussain and Praveen Paruchuri and Sujit Gujar},
year = {2022},
date = {2022-02-22},
booktitle = {36th AAAI Conference on Artificial Intelligence },
publisher = {AAAI},
keywords = {differential privacy, inverse reinforcement learning, Reinforcement Learning},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 2. | Sankarshan Damle; Aleksei Triastcyn; Boi Faltings; Sujit Gujar: Differentially Private Multi-Agent Constraint Optimization. To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '21), Forthcoming. (Type: Conference | BibTeX | Tags: differential privacy, distributed constrained optimization)@conference{Damle2021c,
title = {Differentially Private Multi-Agent Constraint Optimization},
author = {Sankarshan Damle and Aleksei Triastcyn and Boi Faltings and Sujit Gujar},
year = {2021},
date = {2021-12-14},
booktitle = {To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '21)},
keywords = {differential privacy, distributed constrained optimization},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 3. | Manisha Padala; Sankarshan Damle ; Sujit Gujar: Federated Learning Meets Fairness and Differential Privacy. In: Proceedings of the 28th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific Neural Network Society 2021 (ICONIP '21), Forthcoming. (Type: Conference | BibTeX | Tags: differential privacy, fairness, federated learning)@inproceedings{Manisha2021b,
title = {Federated Learning Meets Fairness and Differential Privacy},
author = {Manisha Padala and Sankarshan Damle, and Sujit Gujar},
year = {2021},
date = {2021-12-08},
publisher = {Proceedings of the 28th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific Neural Network Society 2021 (ICONIP '21)},
keywords = {differential privacy, fairness, federated learning},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 4. | Sankarshan Damle; Moin Hussain Moti; Praphul Chandra; Sujit Gujar: Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding. 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2021) , Forthcoming. (Type: Conference | BibTeX | Tags: Blockchains, Civic Crowdfunding, differential privacy, fairness, federated learning)@conference{sankarshan2021b,
title = {Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding},
author = {Sankarshan Damle and Moin Hussain Moti and Praphul Chandra and Sujit Gujar},
year = {2021},
date = {2021-11-08},
booktitle = {18th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2021) },
keywords = {Blockchains, Civic Crowdfunding, differential privacy, fairness, federated learning},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 5. | Padala Manisha; Sankarshan Damle; Sujit Gujar: Building Ethical AI: Federated Learning meets Fairness and Privacy. First Indian Conference on Deployable AI, 2021. (Type: Conference | BibTeX | Tags: differential privacy, fairness, federated learning)@conference{manisha2021a,
title = {Building Ethical AI: Federated Learning meets Fairness and Privacy},
author = {Padala Manisha and Sankarshan Damle and Sujit Gujar },
year = {2021},
date = {2021-06-17},
booktitle = {First Indian Conference on Deployable AI},
keywords = {differential privacy, fairness, federated learning},
pubstate = {published},
tppubtype = {conference}
}
|
| 6. | Sankarshan Damle; Aleksei Triastcyn; Boi Faltings; Sujit Gujar: Differentially Private Multi-Agent Constraint Optimization. The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21), 2021. (Type: Workshop | Abstract | Links | BibTeX | Tags: differential privacy)@workshop{Damle2021b,
title = {Differentially Private Multi-Agent Constraint Optimization},
author = {Sankarshan Damle and Aleksei Triastcyn and Boi Faltings and Sujit Gujar},
editor = {Ferdinando Fioretto},
url = {https://ppai21.github.io/files/38-paper.pdf},
year = {2021},
date = {2021-02-08},
booktitle = {The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)},
abstract = {Several optimization scenarios involve multiple agents that
desire to protect the privacy of their preferences. There ex-
ist distributed algorithms for constraint optimization that pro-
vide improved privacy protection through secure multiparty
computation. However, it comes at the expense of high com-
putational complexity and does not constitute a rigorous pri-
vacy guarantee, as the result of the computation itself may
compromise agents’ preferences. In this work, we show how
to achieve differential privacy through randomization of the
solving process. In particular, we present P-Gibbs, which
adapts the SD-Gibbs algorithm to obtain differential privacy
guarantees with much higher computational efficiency. Ex-
periments on graph coloring and meeting scheduling show
the algorithm’s privacy-performance trade-off compared to
variants with uniform sampling and the SD-Gibbs algorithm.},
keywords = {differential privacy},
pubstate = {published},
tppubtype = {workshop}
}
Several optimization scenarios involve multiple agents that
desire to protect the privacy of their preferences. There ex-
ist distributed algorithms for constraint optimization that pro-
vide improved privacy protection through secure multiparty
computation. However, it comes at the expense of high com-
putational complexity and does not constitute a rigorous pri-
vacy guarantee, as the result of the computation itself may
compromise agents’ preferences. In this work, we show how
to achieve differential privacy through randomization of the
solving process. In particular, we present P-Gibbs, which
adapts the SD-Gibbs algorithm to obtain differential privacy
guarantees with much higher computational efficiency. Ex-
periments on graph coloring and meeting scheduling show
the algorithm’s privacy-performance trade-off compared to
variants with uniform sampling and the SD-Gibbs algorithm. |
