E. Paul Ratazzi

Rui Hu, Yuanxiong Guo, E. Paul. Ratazzi et al.

With the proliferation of smart devices having built-in sensors, Internet connectivity, and programmable computation capability in the era of Internet of things (IoT), tremendous data is being generated at the network edge. Federated learning is capable of analyzing the large amount of data from a distributed set of smart devices without requiring them to upload their data to a central place. However, the commonly-used federated learning algorithm is based on stochastic gradient descent (SGD) and not suitable for resource-constrained IoT environments due to its high communication resource requirement. Moreover, the privacy of sensitive data on smart devices has become a key concern and needs to be protected rigorously. This paper proposes a novel federated learning framework called DP-PASGD for training a machine learning model efficiently from the data stored across resource-constrained smart devices in IoT while guaranteeing differential privacy. The optimal schematic design of DP-PASGD that maximizes the learning performance while satisfying the limits on resource cost and privacy loss is formulated as an optimization problem, and an approximate solution method based on the convergence analysis of DP-PASGD is developed to solve the optimization problem efficiently. Numerical results based on real-world datasets verify the effectiveness of the proposed DP-PASGD scheme.

Xi Hang Cao, Xiaojiang Du, E. Paul Ratazzi

Internet of Things (IoT) is ubiquitous because of its broad applications and the advance in communication technologies. The capabilities of IoT also enable its important role in homeland security and tactical missions, including Reconnaissance, Intelligence, Surveillance, and Target Acquisition (RISTA). IoT security becomes the most critical issue before its extensive use in military operations. While the majority of research focuses on smart IoT devices, treatments for legacy dumb network-ready devices are lacking; moreover, IoT devices deployed in a hostile environment are often required to be dumb due to the strict hardware constraints, making them highly vulnerable to cyber attacks. To mitigate the problem, we propose a light-weight authentication scheme for dumb IoT devices, in a case study of the UAV-sensor collaborative RISTA missions. Our scheme utilizes the covert channels in the physical layer for authentications and does not request conventional key deployments, key generations which may cause security risks and large overhead that a dumb sensor cannot afford. Our scheme operates on the physical layer, and thus it is highly portable and generalizable to most commercial and military communication protocols. We demonstrate the viability of our scheme by building a prototype system and conducting experiments to emulate the behaviors of UAVs and sensors in real scenarios.

Paul Ratazzi, Ashok Bommisetti, Nian Ji et al.

Virtualization is frequently used to isolate untrusted processes and control their access to sensitive resources. However, isolation usually carries a price in terms of less resource sharing and reduced inter-process communication. In an open architecture such as Android, this price and its impact on performance, usability, and transparency must be carefully considered. Although previous efforts in developing general-purpose isolation solutions have shown that some of these negative side effects can be mitigated, doing so involves overcoming significant design challenges by incorporating numerous additional platform complexities not directly related to improved security. Thus, the general purpose solutions become inefficient and burdensome if the end-user has only specific security goals. In this paper, we present PINPOINT, a resource isolation strategy that forgoes general-purpose solutions in favor of a "building block" approach that addresses specific end-user security goals. PINPOINT embodies the concept of Linux Namespace lightweight isolation, but does so in the Android Framework by guiding the security designer towards isolation points that are contextually close to the resource(s) that need to be isolated. This strategy allows the rest of the Framework to function fully as intended, transparently. We demonstrate our strategy with a case study on Android System Services, and show four applications of PINPOINTed system services functioning with unmodified market apps. Our evaluation results show that practical security and privacy advantages can be gained using our approach, without inducing the problematic side-effects that other general-purpose designs must address.

Jingwei Liu, Xiaolu Li, Rong Sun et al.

Along with the development of wireless communication technology, a mass of mobile devices are gaining stronger sensing capability, which brings a novel paradigm to light: participatory sensing networks (PSNs). PSNs can greatly reduce the cost of wireless sensor networks, and hence are becoming an efficient way to obtain abundant sensing data from surrounding environment. Therefore, PSNs would lead to significant improvement in various fields, including cognitive communication. However, the large-scale deployment of participatory sensing applications is hindered by the lack of incentive mechanism, security and privacy concerns. It is still an ongoing issue to address all three aspects simultaneously in PSNs. In this paper, we construct an efficient privacy-preserving incentive scheme without trusted third party (TTP) for PSNs to motivate user-participation. This scheme allows each participant to earn credits by contributing data privately. Using blind and partially blind signatures, the proposed scheme is proved to be secure for privacy and incentive. Additionally, the performance evaluation in terms of computation and storage indicates that the proposed scheme has higher efficiency.

Haotian Chi, Longfei Wu, Xiaojiang Du et al.

To facilitate monitoring and management, modern Implantable Medical Devices (IMDs) are often equipped with wireless capabilities, which raise the risk of malicious access to IMDs. Although schemes are proposed to secure the IMD access, some issues are still open. First, pre-sharing a long-term key between a patient's IMD and a doctor's programmer is vulnerable since once the doctor's programmer is compromised, all of her patients suffer; establishing a temporary key by leveraging proximity gets rid of pre-shared keys, but as the approach lacks real authentication, it can be exploited by nearby adversaries or through man-in-the-middle attacks. Second, while prolonging the lifetime of IMDs is one of the most important design goals, few schemes explore to lower the communication and computation overhead all at once. Finally, how to safely record the commands issued by doctors for the purpose of forensics, which can be the last measure to protect the patients' rights, is commonly omitted in the existing literature. Motivated by these important yet open problems, we propose an innovative scheme e-SAFE, which significantly improves security and safety, reduces the communication overhead and enables IMD-access forensics. We present a novel lightweight compressive sensing based encryption algorithm to encrypt and compress the IMD data simultaneously, reducing the data transmission overhead by over 50% while ensuring high data confidentiality and usability. Furthermore, we provide a suite of protocols regarding device pairing, dual-factor authentication, and accountability-enabled access. The security analysis and performance evaluation show the validity and efficiency of the proposed scheme.

Paul Ratazzi, Yousra Aafer, Amit Ahlawat et al.

Like many desktop operating systems in the 1990s, Android is now in the process of including support for multi-user scenarios. Because these scenarios introduce new threats to the system, we should have an understanding of how well the system design addresses them. Since the security implications of multi-user support are truly pervasive, we developed a systematic approach to studying the system and identifying problems. Unlike other approaches that focus on specific attacks or threat models, ours systematically identifies critical places where access controls are not present or do not properly identify the subject and object of a decision. Finding these places gives us insight into hypothetical attacks that could result, and allows us to design specific experiments to test our hypothesis. Following an overview of the new features and their implementation, we describe our methodology, present a partial list of our most interesting hypotheses, and describe the experiments we used to test them. Our findings indicate that the current system only partially addresses the new threats, leaving the door open to a number of significant vulnerabilities and privacy issues. Our findings span a spectrum of root causes, from simple oversights, all the way to major system design problems. We conclude that there is still a long way to go before the system can be used in anything more than the most casual of sharing environments.