Nan Zhang

Haojun Yu, Youcheng Li, Nan Zhang et al.

Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development to breast ultrasound analysis remains untapped. In this paper, we present BUSGen, the first foundational generative model specifically designed for breast ultrasound image analysis. Pretrained on over 3.5 million breast ultrasound images, BUSGen has acquired extensive knowledge of breast structures, pathological features, and clinical variations. With few-shot adaptation, BUSGen can generate repositories of realistic and informative task-specific data, facilitating the development of models for a wide range of downstream tasks. Extensive experiments highlight BUSGen's exceptional adaptability, significantly exceeding real-data-trained foundational models in breast cancer screening, diagnosis, and prognosis. In breast cancer early diagnosis, our approach outperformed all board-certified radiologists (n=9), achieving an average sensitivity improvement of 16.5% (P-value<0.0001). Additionally, we characterized the scaling effect of using generated data which was as effective as the collected real-world data for training diagnostic models. Moreover, extensive experiments demonstrated that our approach improved the generalization ability of downstream models. Importantly, BUSGen protected patient privacy by enabling fully de-identified data sharing, making progress forward in secure medical data utilization. An online demo of BUSGen is available at https://aibus.bio.

Nan Zhang, Xianghang Mi, Xuan Feng et al.

Virtual personal assistants (VPA) (e.g., Amazon Alexa and Google Assistant) today mostly rely on the voice channel to communicate with their users, which however is known to be vulnerable, lacking proper authentication. The rapid growth of VPA skill markets opens a new attack avenue, potentially allowing a remote adversary to publish attack skills to attack a large number of VPA users through popular IoT devices such as Amazon Echo and Google Home. In this paper, we report a study that concludes such remote, large-scale attacks are indeed realistic. More specifically, we implemented two new attacks: voice squatting in which the adversary exploits the way a skill is invoked (e.g., "open capital one"), using a malicious skill with similarly pronounced name (e.g., "capital won") or paraphrased name (e.g., "capital one please") to hijack the voice command meant for a different skill, and voice masquerading in which a malicious skill impersonates the VPA service or a legitimate skill to steal the user's data or eavesdrop on her conversations. These attacks aim at the way VPAs work or the user's mis-conceptions about their functionalities, and are found to pose a realistic threat by our experiments (including user studies and real-world deployments) on Amazon Echo and Google Home. The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built. We further developed techniques for automatic detection of these attacks, which already capture real-world skills likely to pose such threats.

Nan Zhang, Soteris Demetriou, Xianghang Mi et al.

Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify over 3000 IoT-related articles. Further, by clustering such collected data using machine learning technologies, we are able to compare academic views with the findings from industry and other sources, in an attempt to understand the gaps between them, the trend of the IoT security risks and new problems that need further attention. We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas. We use this taxonomy as a beacon to assess each IoT work across a number of properties we define. Our assessment reveals that relevant security and privacy problems are far from solved. We discuss how each proposed solution can be applied to a problem area and highlight their strengths, assumptions and constraints. We stress the need for a security framework for IoT vendors and discuss the trend of shifting security liability to external or centralized entities. We also identify open research problems and provide suggestions towards a secure IoT ecosystem.

Soteris Demetriou, Nan Zhang, Yeonjoon Lee et al.

A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). Those systems tend to rely on the Wi-Fi router to authenticate other devices; as verified in our study, IoT vendors tend to trust all devices connected to the HAN. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control, as confirmed in our measurement study. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called Hanguard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. Hanguard achieves a fine-grained, per-app protection through bridging the OS-level situation awareness and the router-level per-flow control: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. Hanguard uses a role-based access control (RBAC) schema which leverages type enforcement (TE) and multi-category security (MCS) primitives to define highly flexible access control rules. We implemented our design over both Android and iOS (>95% of mobile OS market share) and a popular router. Our study shows that Hanguard is both efficient and effective in practice.