Yih-Chun Hu

Hsin-Tien Chiang, Yi-Chiao Wu, Cheng Yu et al.

Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. Recently, deep neural network (DNN) models have been applied to build non-intrusive speech assessment approaches and confirmed to provide promising performance. However, most DNN-based approaches are designed for normal-hearing listeners without considering hearing-loss factors. In this study, we propose a DNN-based hearing aid speech assessment network (HASA-Net), formed by a bidirectional long short-term memory (BLSTM) model, to predict speech quality and intelligibility scores simultaneously according to input speech signals and specified hearing-loss patterns. To the best of our knowledge, HASA-Net is the first work to incorporate quality and intelligibility assessments utilizing a unified DNN-based non-intrusive model for hearing aids. Experimental results show that the predicted speech quality and intelligibility scores of HASA-Net are highly correlated to two well-known intrusive hearing-aid evaluation metrics, hearing aid speech quality index (HASQI) and hearing aid speech perception index (HASPI), respectively.

Bo-Rong Chen, Yih-Chun Hu

Due to the recent outbreak of COVID-19, many governments suspended outdoor activities and imposed social distancing policies to prevent the transmission of SARS-CoV-2. These measures have had severe impact on the economy and peoples' daily lives. An alternative to widespread lockdowns is effective contact tracing during an outbreak's early stage. However, mathematical models suggest that epidemic control for SARS-CoV-2 transmission with manual contact tracing is implausible. To reduce the effort of contact tracing, many digital contact tracing projects (e.g., PEPP-PT, DP-3T, TCN, BlueTrace, Google/Apple Exposure Notification, and East/West Coast PACT) are being developed to supplement manual contact tracing. However, digital contact tracing has drawn scrutiny from privacy advocates, since governments or other parties may attempt to use contact tracing protocols for mass surveillance. As a result, many digital contact tracing projects build privacy-preserving mechanisms to limit the amount of privacy-sensitive information leaked by the protocol. In this paper, we examine how these architectures resist certain classes of attacks, specifically DoS attacks, and present BlindSignedIDs, a privacy-preserving digital contact tracing mechanism, which are verifiable ephemeral identifiers to limit the effectiveness of MAC-compliant DoS attacks. In our evaluations, we showed BlindSignedID can effectively deny bogus EphIDs, mitigating DoS attacks on the local storage beyond 90% of stored EphIDs. Our example DoS attacks showed that using 4 attackers can cause the gigabyte level DoS attacks within normal working hours and days.

Zhuotao Liu, Yangxi Xiang, Jian Shi et al.

Blockchain interoperability, which allows state transitions across different blockchain networks, is critical functionality to facilitate major blockchain adoption. Existing interoperability protocols mostly focus on atomic token exchange between blockchains. However, as blockchains have been upgraded from passive distributed ledgers into programmable state machines (thanks to smart contracts), the scope of blockchain interoperability goes beyond just token exchange. In this paper, we present HyperService, the first platform that delivers interoperability and programmability across heterogeneous blockchains. HyperService is powered by two innovative designs: (i) a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and (ii) a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in about 35,000 lines of code to demonstrate its practicality. Our experiment results show that HyperService imposes reasonable latency, in order of seconds, on the end-to-end execution of cross-chain applications

Subhash Lakshminarayana, Jabir Shabbir Karachiwala, Sang-Yoon Chang et al.

We study the impact of signal jamming attacks against the communication based train control (CBTC) systems and develop the countermeasures to limit the attacks' impact. CBTC supports the train operation automation and moving-block signaling, which improves the transport efficiency. We consider an attacker jamming the wireless communication between the trains or the train to wayside access point, which can disable CBTC and the corresponding benefits. In contrast to prior work studying jamming only at the physical or link layer, we study the real impact of such attacks on end users, namely train journey time and passenger congestion. Our analysis employs a detailed model of leaky medium-based communication system (leaky waveguide or leaky feeder/coaxial cable) popularly used in CBTC systems. To counteract the jamming attacks, we develop a mitigation approach based on frequency hopping spread spectrum taking into account domain-specific structure of the leaky-medium CBTC systems. Specifically, compared with existing implementations of FHSS, we apply FHSS not only between the transmitter-receiver pair but also at the track-side repeaters. To demonstrate the feasibility of implementing this technology in CBTC systems, we develop a FHSS repeater prototype using software-defined radios on both leaky-medium and open-air (free-wave) channels. We perform extensive simulations driven by realistic running profiles of trains and real-world passenger data to provide insights into the jamming attack's impact and the effectiveness of the proposed countermeasure.

Zhuotao Liu, Yushan Liu, Philipp Winter et al.

Tor is the most widely used anonymity network, currently serving millions of users each day. However, there is no access control in place for all these users, leaving the network vulnerable to botnet abuse and attacks. For example, criminals frequently use exit relays as stepping stones for attacks, causing service providers to serve CAPTCHAs to exit relay IP addresses or blacklisting them altogether, which leads to severe usability issues for legitimate Tor users. To address this problem, we propose TorPolice, the first privacy-preserving access control framework for Tor. TorPolice enables abuse-plagued service providers such as Yelp to enforce access rules to police and throttle malicious requests coming from Tor while still providing service to legitimate Tor users. Further, TorPolice equips Tor with global access control for relays, enhancing Tor's resilience to botnet abuse. We show that TorPolice preserves the privacy of Tor users, implement a prototype of TorPolice, and perform extensive evaluations to validate our design goals.