Supriyo Chakraborty
Publications 70
2019 in ICML (International Conference on Machine Learning)
#1Arjun Nitin Bhagoji (Princeton University)H-Index: 7
#2Supriyo Chakraborty (IBM)H-Index: 11
Last.Seraphin B. Calo (IBM)H-Index: 22
view all 4 authors...
#1Changchang LiuH-Index: 4
Last.Dinesh C. VermaH-Index: 22
view all 4 authors...
Distributed learning has emerged as a useful tool for analyzing data stored in multiple geographic locations, especially when the distributed data sets are large and hard to move around, or the data owner is reluctant to put data into the Cloud due to privacy concerns. In distributed learning, only the locally computed models are uploaded to the fusion server, which however may still cause privacy issues since the fusion server could implement various inference attacks from its observations. To ...
Jan 1, 2019 in AAAI (National Conference on Artificial Intelligence)
#1Amanda Coston (CMU: Carnegie Mellon University)H-Index: 1
Last.Supriyo Chakraborty (IBM)H-Index: 11
view all 7 authors...
#1Moustafa Alzantot (UCLA: University of California, Los Angeles)H-Index: 8
#2Yash Sharma (Cooper Union)H-Index: 5
Last.Mani B. Srivastava (UCLA: University of California, Los Angeles)H-Index: 89
view all 6 authors...
#1Yeon-sup Lim (IBM)H-Index: 9
#2Mudhakar Srivatsa (IBM)H-Index: 22
Last.Ian Taylor (Cardiff University)H-Index: 33
view all 4 authors...
Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our res...
#1Matthew P. Johnson (CUNY: City University of New York)H-Index: 19
#2Liang Zhao (CUNY: City University of New York)H-Index: 6
Last.Supriyo Chakraborty (IBM)H-Index: 11
view all 3 authors...
We study a fine-grained model in which a perturbed version of some data ( D) is to be disclosed, with the aims of permitting the receiver to accurately infer some useful aspects ( X=f(D)) of it, while preventing her from inferring other private aspects ( Y=g(D)). Correlation between the bases for these inferences necessitates compromise between these goals. Determining exactly how the disclosure ( M) will be probabilistically generated (from D), somehow trading off between making ...
#1Federico Cerutti (Cardiff University)H-Index: 11
#2Moustafa Alzantot (UCLA: University of California, Los Angeles)H-Index: 8
Last.Alun David Preece (Cardiff University)H-Index: 31
view all 10 authors...
In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight-i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight-i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic ap...
#1Richard Tomsett (IBM)H-Index: 3
Last.Mani B. Srivastava (UCLA: University of California, Los Angeles)H-Index: 89
view all 8 authors...
Recent advances in Machine Learning (ML) have profoundly changed many detection, classification, recognition and inference tasks. Given the complexity of the battlespace, ML has the potential to revolutionise how Coalition Situation Understanding is synthesised and revised. However, many issues must be overcome before its widespread adoption. In this paper we consider two - interpretability and adversarial attacks. Interpretability is needed because military decision-makers must be able to justi...