arXiv subjects

Yanjiao Chen

Publications and source records attributed to Yanjiao Chen.

21 records · Page 2Linked to original sources

arXiv2021

FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants

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 small set of pre-registered wake-up words are detected. However, voice assistants are shown to be vulnerable to the FakeWake phenomena, whereby they are inadvertently triggered by innocent-sounding fuzzy words. In this paper, we present a systematic investigation of the FakeWake phenomena from three aspects. To start with, we design the first fuzzy word generator to automatically and efficiently produce fuzzy words instead of searching through a swarm of audio materials. We manage to generate 965 fuzzy words covering 8 most popular English and Chinese smart speakers. To explain the causes underlying the FakeWake phenomena, we construct an interpretable tree-based decision model, which reveals phonetic features that contribute to false acceptance of fuzzy words by wake-up word detectors. Finally, we propose remedies to mitigate the effect of FakeWake. The results show that the strengthened models are not only resilient to fuzzy words but also achieve better overall performance on original training datasets.

cs.LG↗

arXiv2020

A Two-stage Game Framework to Secure Transmission in Two-Tier UAV Networks

The multi-UAV network is promising to extend conventional networks by providing broader coverage and better reliability. Nevertheless, the broadcast nature of wireless signals and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs) and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm and an OCF algorithm are proposed to solve these two sequential games whose convergences and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms and the effectiveness of our two-stage game framework in the terms of secrecy performance.

cs.GT↗

arXiv2019

Privacy-Preserving Collaborative Deep Learning with Unreliable Participants

With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc. While deep learning has been increasingly popular, the problem of privacy leakage becomes more and more urgent. Given the fact that the training data may contain highly sensitive information, e.g., personal medical records, directly sharing them among the users (i.e., participants) or centrally storing them in one single location may pose a considerable threat to user privacy. In this paper, we present a practical privacy-preserving collaborative deep learning system that allows users to cooperatively build a collective deep learning model with data of all participants, without direct data sharing and central data storage. In our system, each participant trains a local model with their own data and only shares model parameters with the others. To further avoid potential privacy leakage from sharing model parameters, we use functional mechanism to perturb the objective function of the neural network in the training process to achieve $ε$-differential privacy. In particular, for the first time, we consider the existence of~\textit{unreliable participants}, i.e., the participants with low-quality data, and propose a solution to reduce the impact of these participants while protecting their privacy. We evaluate the performance of our system on two well-known real-world datasets for regression and classification tasks. The results demonstrate that the proposed system is robust against unreliable participants, and achieves high accuracy close to the model trained in a traditional centralized manner while ensuring rigorous privacy protection.

cs.CR↗