In the area of Internet of Things (IoT), voice assistants have become an important interface to operate smart speakers, smartphones, and even automobiles. To save power and protect user privacy, voice assistants send commands to the cloud only if a ...

In this work, we propose a novel problem formulation for de-identification of unstructured clinical text. We formulate the de-identification problem as a sequence to sequence learning problem instead of a token classification problem. Our approach is ...

Dery et al. recently proposed a secure voting protocol for score-based elections, where independent talliers perform the tallying procedure. The protocol offers perfect ballot secrecy: it outputs the identity of the winner(s), but keeps all other ...

We address the problem of multiparty private set intersection against a malicious adversary. First, we show that when one can assume no collusion amongst corrupted parties then there exists an extremely efficient protocol given only symmetric-key ...

This paper presents a new privacy threat, the Invisible Infrared Shadow Attack (IRSA), which leverages the inconspicuous infrared (IR) light emitted by indoor security cameras, to reveal in-home human activities behind opaque curtains. The key ...

Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables ...

Private learning of top-k, i.e., the k most frequent values also called heavy hitters, is a common industry scenario: Companies want to privately learn, e.g., frequently typed new words to improve suggestions on mobile devices, often used browser ...

Verifiability is a key requirement for electronic voting. However, the use of cryptographic techniques to achieve it usually requires specialist knowledge to understand; hence voters cannot easily assess the validity of such arguments themselves. To ...

While privacy laws and regulations require apps and services to disclose the purposes of their data collection to the users (i.e., why do they collect my data?), the data usage in an app's actual behavior does not always comply with the purposes stated ...

It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal representations extracted by the classifier. We take a step forward and show ...