arXiv subjects

Ankur Taly

Publications and source records attributed to Ankur Taly.

22 records · Page 2Linked to original sources

arXiv2017

Axiomatic Attribution for Deep Networks

We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms---Sensitivity and Implementation Invariance that attribution methods ought to satisfy. We show that they are not satisfied by most known attribution methods, which we consider to be a fundamental weakness of those methods. We use the axioms to guide the design of a new attribution method called Integrated Gradients. Our method requires no modification to the original network and is extremely simple to implement; it just needs a few calls to the standard gradient operator. We apply this method to a couple of image models, a couple of text models and a chemistry model, demonstrating its ability to debug networks, to extract rules from a network, and to enable users to engage with models better.

cs.LG↗

arXiv2017

Privacy, Discovery, and Authentication for the Internet of Things

Automatic service discovery is essential to realizing the full potential of the Internet of Things (IoT). While discovery protocols like Multicast DNS, Apple AirDrop, and Bluetooth Low Energy have gained widespread adoption across both IoT and mobile devices, most of these protocols do not offer any form of privacy control for the service, and often leak sensitive information such as service type, device hostname, device owner's identity, and more in the clear. To address the need for better privacy in both the IoT and the mobile landscape, we develop two protocols for private service discovery and private mutual authentication. Our protocols provide private and authentic service advertisements, zero round-trip (0-RTT) mutual authentication, and are provably secure in the Canetti-Krawczyk key-exchange model. In contrast to alternatives, our protocols are lightweight and require minimal modification to existing key-exchange protocols. We integrate our protocols into an existing open-source distributed applications framework, and provide benchmarks on multiple hardware platforms: Intel Edisons, Raspberry Pis, smartphones, laptops, and desktops. Finally, we discuss some privacy limitations of the Apple AirDrop protocol (a peer-to-peer file sharing mechanism) and show how to improve the privacy of Apple AirDrop using our private mutual authentication protocol.

cs.CR↗

arXiv2016

Distributed Authorization in Vanadium

In this tutorial, we present an authorization model for distributed systems that operate with limited internet connectivity. Reliable internet access remains a luxury for a majority of the world's population. Even for those who can afford it, a dependence on internet connectivity may lead to sub-optimal user experiences. With a focus on decentralized deployment, we present an authorization model that is suitable for scenarios where devices right next to each other (such as a sensor or a friend's phone) should be able to communicate securely in a peer-to-peer manner. The model has been deployed as part of an open-source distributed application framework called Vanadium. As part of this tutorial, we survey some of the key ideas and techniques used in distributed authorization, and explain how they are combined in the design of our model.

cs.CR↗

arXiv2016

Gradients of Counterfactuals

Gradients have been used to quantify feature importance in machine learning models. Unfortunately, in nonlinear deep networks, not only individual neurons but also the whole network can saturate, and as a result an important input feature can have a tiny gradient. We study various networks, and observe that this phenomena is indeed widespread, across many inputs. We propose to examine interior gradients, which are gradients of counterfactual inputs constructed by scaling down the original input. We apply our method to the GoogleNet architecture for object recognition in images, as well as a ligand-based virtual screening network with categorical features and an LSTM based language model for the Penn Treebank dataset. We visualize how interior gradients better capture feature importance. Furthermore, interior gradients are applicable to a wide variety of deep networks, and have the attribution property that the feature importance scores sum to the the prediction score. Best of all, interior gradients can be computed just as easily as gradients. In contrast, previous methods are complex to implement, which hinders practical adoption.

cs.LG↗