Speech serves as a ubiquitous input interface for embedded mobile devices. Cloud-based solutions, while offering powerful speech understanding services, raise significant concerns regarding user privacy. To address this, disentanglement-based encoders have been proposed to remove sensitive information from speech signals without compromising the speech understanding functionality. However, these encoders demand high memory usage and computation complexity, making them impractical for resource-constrained wimpy devices.Our solution is based on a key observation that speech understanding hinges on long-term dependency knowledge of the entire utterance, in contrast to privacy-sensitive elements that are short-term dependent. Exploiting this observation, we propose SILENCE, a lightweight system that selectively obscuring short-term details, without damaging the long-term dependent speech understanding performance.The crucial part of SILENCE is a differential mask generator derived from interpretable learning to automatically configure the masking process.We have implemented SILENCE on the STM32H7 microcontroller and evaluate its efficacy under different attacking scenarios.

Our world is undergoing an information Big Bang, in which the universe of data doubles every two years and quintillions of bytes of data are generated every day.1 For decades, Moore's Law on the doubling of computing power every 18-24 months has driven the growth of information technology. Now–as billions of smartphones and other devices collect and transmit data over high-speed global networks, store data in ever-larger data centers, and analyze it using increasingly powerful and sophisticated software–Metcalfe's Law comes into play. It treats the value of networks as a function of the square of the number of nodes, meaning that network effects exponentially compound this historical growth in information. As 5G networks and eventually quantum computing deploy, this data explosion will grow even faster and bigger. The impact of big data is commonly described in terms of three "Vs": volume, variety, and velocity.2

Even though "de-identified," patient data can still sometimes be revealed by attackers. The focus of this program will include technical and policy measures that might better protect the privacy of electronic health records (EHRs) when they are used for machine learning. The approach to be discussed includes multivariate models computed in a decentralized fashion for a large clinical data research network, and how to collaborate in developing sound methods to protect patient privacy. Sharing according to patient instructions is one important way to conduct responsible machine learning. This presentation will include results from a recent study on patient-controlled electronic healthcare data sharing.

As large-scale theft of data from corporate servers is becoming increasingly common, it becomes interesting to examine alternatives to the paradigm of centralizing sensitive data into large databases. Instead, one could use cryptography and distributed computation so that sensitive data can be supplied and processed in encrypted form, and only the final result is made known. In this paper, we examine how such a paradigm can be used to implement constraint satisfaction, a technique that can solve a broad class of AI problems such as resource allocation, planning, scheduling, and diagnosis. Most previous work on privacy in constraint satisfaction only attempted to protect specific types of information, in particular the feasibility of particular combinations of decisions. We formalize and extend these restricted notions of privacy by introducing four types of private information, including the feasibility of decisions and the final decisions made, but also the identities of the participants and the topology of the problem. We present distributed algorithms that allow computing solutions to constraint satisfaction problems while maintaining these four types of privacy. We formally prove the privacy properties of these algorithms, and show experiments that compare their respective performance on benchmark problems.