More Like this

1. TEO: Ephemeral Ownership for IoT Devices to Provide Granular Data Control

   Han Zhang, Yuvraj Agarwal (January 2022, 20th ACM International Conference on Mobile Systems, Applications, and Services)

   As Internet-of-Things (IoT) devices rapidly gain popularity, they raise significant privacy concerns given the breadth of sensitive data they can capture. These concerns are amplified by the fact that in many situations, IoT devices collect data about people other than their owner or administrator, and these stakeholders have no say in how that data is managed, used, or shared. To address this, we propose a new model of ownership, IoT Ephemeral Ownership (TEO). TEO allows stakeholders to quickly register with an IoT device for a limited period, and thus claim co-ownership over the sensitive data that the device generates. Device admins retain the ability to decide who may become an ephemeral owner, but no longer have access or control to the private data generated by the device. The encrypted data in TEO is accessible only by entities after seeking explicit permission from the different co-owners of that data. We verify the key security properties of our protocol underpinning TEO in the symbolic model using ProVerif. We also implement a cross-platform prototype of TEO for mobile phones and embedded devices, and integrate it into three real-world application case studies. Our evaluation shows that the latency and battery impact of TEO is typically small, adding ≤187 ms onto one-time operations, and introducing limited (<25%) overhead on recurring operations like private data storage. 

   more » « less
2. By Your Command: Extracting the User Actions that Create Network Flows in Android

   Liu, Shuwen; Petitti, Joseph P.; Lei, Yunsen Lei; Liu, Yu; Shue, Craig A. (January 2023, International Conference on the Network of the Future)

   Given the complexity of modern systems, it can be difficult for device defenders to pinpoint the user action that precipitates a network connection. Mobile devices, such as smartphones, further complicate analysis since they may have diverse and ephemeral network connectivity and support users in both personal and professional capacities. There are multiple stakeholders associated with mobile devices, such as the end-user, device owner, and each organization whose assets are accessed via the device; however, none may be able to fully manage, troubleshoot, or defend the device on their own. In this work, we explore a set of techniques to determine the root cause of each new network flow, such the button press or gesture for user-initiated flows, associated with a mobile device. We fuse the User Interface (UI) context with network flow data to enhance network profiling on the Android operating system. In doing so, we find that we can improve network profiling by clearly linking user actions with network behavior. When exploring effectiveness, the system enables allow-lists to reach over 99% accuracy, even when user-specified destinations are used. 

   more » « less
3. Energy-Aware Digital Signatures for Embedded Medical Devices

   https://doi.org/10.1109/CNS.2019.8802675  

   Ozmen, Muslum Ozgur; Yavuz, Attila A.; Behnia, Rouzbeh (June 2019, IEEE Conference on Communications and Network Security (CNS))

   Authentication is vital for the Internet of Things (IoT) applications involving sensitive data (e.g., medical and financial systems). Digital signatures offer scalable authentication with non-repudiation and public verifiability, which are necessary for auditing and dispute resolution in such IoT applications. However, digital signatures have been shown to be highly costly for low-end IoT devices, especially when embedded devices (e.g., medical implants) must operate without a battery replacement for a long time. We propose an Energy-aware Signature for Embedded Medical devices (ESEM) that achieves near-optimal signer efficiency. ESEM signature generation does not require any costly operations (e.g., elliptic curve (EC) scalar multiplication/addition), but only a small constant-number of pseudo-random function calls, additions, and a single modular multiplication. ESEM has the smallest signature size among its EC-based counterparts with an identical private key size. We achieve this by eliminating the use of the ephemeral public key (i.e, commitment) in Schnorrtype signatures from the signing via a distributed construction at the verifier without interaction with the signer while permitting a constant-size public key. We proved that ESEM is secure (in random oracle model), and fully implemented it on an 8-bit AVR microcontroller that is commonly used in medical devices. Our experiments showed that ESEM achieves 8.4× higher energy efficiency over its closest counterpart while offering a smaller signature and code size. Hence, ESEM can be suitable for deployment on resource-limited embedded devices in IoT. We 

   more » « less
4. Understanding Dark Patterns in Home IoT Devices

   https://doi.org/10.1145/3544548.3581432  

   Kowalczyk, Monica; Gunawan, Johanna T.; Choffnes, David; Dubois, Daniel J; Hartzog, Woodrow; Wilson, Christo (April 2023, CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems)

   Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits. Speakers, doorbells, and camera devices contain the most dark patterns, with manufacturers of such devices (Amazon and Google) having the most dark patterns compared to other vendors. We investigate how this distribution impacts the potential for consumer exposure to dark patterns, discuss broader implications for key stakeholders like designers and regulators, and identify opportunities for future dark patterns study. 

   more » « less
5. Safeguarding User-Centric Privacy in Smart Homes

   https://doi.org/10.1145/3701726  

   Yu, Keyang; Li, Qi; Chen, Dong; Hu, Liting (November 2024, ACM Transactions on Internet Technology)

   Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving. 

   more » « less