Published in Proceedings on Privacy Enhancing Technologies
Existing anonymity systems sacrifice anonymity for efficient communication or vice-versa. Onion-routing achieves low latency, high bandwidth, and scalable anonymous communication, but is susceptible to traffic analysis attacks. Designs based on DC-Nets, on the other hand, protect the users against traffic analysis attacks, but sacrifice bandwidth. ...
Published in Proceedings on Privacy Enhancing Technologies
We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning a...
Published in Proceedings on Privacy Enhancing Technologies
Image hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. These concerns are further exacerbated with the advent of Convolutional Neural Networks (CNNs) that can be trained on available images to automatically detect and...
Published in Proceedings on Privacy Enhancing Technologies
Abstract: Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used, and often blindly trust the app developers. In this paper, we present IoTWATcH, a dynamic analysis tool that uncovers...
Published in Proceedings on Privacy Enhancing Technologies
With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contrib...
Published in Proceedings on Privacy Enhancing Technologies
In a Functional Encryption scheme (FE), a trusted authority enables designated parties to compute specific functions over encrypted data. As such, FE promises to break the tension between industrial interest in the potential of data mining and user concerns around the use of private data. FE allows the authority to decide who can compute and what c...
Published in Proceedings on Privacy Enhancing Technologies
Recent work on Renyi Differential Privacy has shown the feasibility of applying differential privacy to deep learning tasks. Despite their promise, however, differentially private deep networks often lag far behind their non-private counterparts in accuracy, showing the need for more research in model architectures, optimizers, etc. One of the barr...
Published in Proceedings on Privacy Enhancing Technologies
Over the past decade, research has explored managing the availability of shared personal online data, with particular focus on longitudinal aspects of privacy. Yet, there is no taxonomy that takes user perspective and technical approaches into account. In this work, we systematize research on longitudinal privacy management of publicly shared perso...
Published in Proceedings on Privacy Enhancing Technologies
We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages – (i) It is highly expressive with support for high capacity networks such as VGG16 (ii) it supports batch normalization which is important for training complex networks such as AlexNe...
Published in Proceedings on Privacy Enhancing Technologies
