382 citations · 726 across the 12 of their papers we have counts for
13 papers
On the amplification of security and privacy risks by post-hoc explanations in machine learning models
Pengrui Quan, Supriyo Chakraborty, Jeya Vikranth Jeyakumar +1
A variety of explanation methods have been proposed in recent years to help users gain insights into the results returned by neural networks, which are otherwise complex and opaque…
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification
Ashwinee Panda, Saeed Mahloujifar, Arjun N. Bhagoji +2
Federated learning is inherently vulnerable to model poisoning attacks because its decentralized nature allows attackers to participate with compromised devices. In model poisoning…
Adversarial training in communication constrained federated learning
Devansh Shah, Parijat Dube, Supriyo Chakraborty +1
Federated learning enables model training over a distributed corpus of agent data. However, the trained model is vulnerable to adversarial examples, designed to elicit misclassific…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
Liam Hiley, Alun Preece, Yulia Hicks +3
A small subset of explainability techniques developed initially for image recognition models has recently been applied for interpretability of 3D Convolutional Neural Network model…
Sanity Checks for Saliency Metrics
Richard Tomsett, Dan Harborne, Supriyo Chakraborty +2
Saliency maps are a popular approach to creating post-hoc explanations of image classifier outputs. These methods produce estimates of the relevance of each pixel to the classifica…
Analyzing Federated Learning through an Adversarial Lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal +1
Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter u…
Stakeholders in Explainable AI
Alun Preece, Dan Harborne, Dave Braines +2
There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general c…
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Richard Tomsett, Dave Braines, Dan Harborne +2
Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is inte…
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty +3
Deep neural networks are vulnerable to adversarial examples, even in the black-box setting, where the attacker is restricted solely to query access. Existing black-box approaches t…
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications
Changchang Liu, Supriyo Chakraborty, Prateek Mittal
Personal sensory data is used by context-aware mobile applications to provide utility. However, the same data can also be used by an adversary to make sensitive inferences about a…
SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation
Moustafa Alzantot, Supriyo Chakraborty, Mani B. Srivastava
Our ability to synthesize sensory data that preserves specific statistical properties of the real data has had tremendous implications on data privacy and big data analytics. The s…
Get More With Less: Near Real-Time Image Clustering on Mobile Phones
Jorge Ortiz, Chien-Chin Huang, Supriyo Chakraborty
Machine learning algorithms, in conjunction with user data, hold the promise of revolutionizing the way we interact with our phones, and indeed their widespread adoption in the des…