Angel Rodriguez, Sara Rampazzi, and Kevin Fu recently had a poster accepted titled IoT Two-Factor Neurometric Authentication System using Wearable EEG:
Abstract: The IoT authentication space suffers from various user-sided drawbacks, such as poor password choice, the accidental publication of biometric data, and the practice of disabling authentication completely. This is commonly attributed to the “Security vs Usability” problem – generally, the stronger the authentication, the more inconvenient it is to perform and maintain for the user. Neurometric authentication offers a compelling resistance to eavesdropping and replay attacks, and the ability for a user to simply “think to unlock”. Furthermore, the recent increase in popularity of consumer EEG devices, as well as new research demonstrating its accuracy, have made EEG-based neurometric authentication much more viable.
Using a Support Vector Machine and one-time tokens, we present a secure two-factor authentication method, that allows a user to authenticate multiple IoT devices. We perform preliminary trials on the Psyionet BCI dataset and demonstrate a qualitative comparison of extracted EEG feature sets.
Left: IoT two factor authentication scheme – (1) After internal user-thought authentication, the device securely sends a one-time token to the IoT device. (2) The IoT device securely communicates with a server to verify the token. (3) If the token is verified, the server sends a secure confirmation reply to the IoT device, authenticating the user. Right: Proof of concept using the Psyionet BCI dataset – The top row shows the averaged covariance matrices of the extracted features of two different users thinking about the same mental task (imagining closing their fists). The bottom row shows similar features for one user thinking of two different tasks (imagine closing both fists vs both feet).
Proceedings of the IEEE Workshop on the Internet of Safe Things (SafeThings), May 2019. Accepted, publication pending.
Timothy J. Pierson, Travis Peters, Ronald Peterson, and David Kotz recently published a paper titled Proximity Detection with Single-Antenna IoT Devices:
Abstract: Providing secure communications between wireless devices that encounter each other on an ad-hoc basis is a challenge that has not yet been fully addressed. In these cases, close physical proximity among devices that have never shared a secret key is sometimes used as a basis of trust; devices in close proximity are deemed trustworthy while more distant devices are viewed as potential adversaries. Because radio waves are invisible, however, a user may believe a wireless device is communicating with a nearby device when in fact the user’s device is communicating with a distant adversary. Researchers have previously proposed methods for multi-antenna devices to ascertain physical proximity with other devices, but devices with a single antenna, such as those commonly used in the Internet of Things, cannot take advantage of these techniques.
We present theoretical and practical evaluation of a method called SNAP – SiNgle Antenna Proximity – that allows a single-antenna Wi-Fi device to quickly determine proximity with another Wi-Fi device. Our proximity detection technique leverages the repeating nature Wi-Fi’s preamble and the behavior of a signal in a transmitting antenna’s near-field region to detect proximity with high probability; SNAP never falsely declares proximity at ranges longer than 14 cm.
Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom), October 2019. ACM Press. Accepted for publication. DOI 10.1145/3300061.3300120.
Aston Zhang, Xun Lu, Carl A. Gunter, Shuochao Yao, Fangbo Tao, Rongda Zhu, Huan Gui, Daniel Fabbri, David Liebovitz, and Bradley Malin recently published a paper titled De Facto Diagnosis Specialties: Recognition and Discovery:
A medical specialty indicates the skills needed by health care providers to conduct key procedures or make critical judgments. However, documentation about specialties may be lacking or inaccurately specified in a health care institution. Thus, we propose to leverage diagnosis histories to recognize medical specialties that exist in practice. Such specialties that are highly recognizable through diagnosis histories are de facto diagnosis specialties. We aim to recognize de facto diagnosis specialties that are listed in the Health Care Provider Taxonomy Code Set (HPTCS) and discover those that are unlisted. First, to recognize the former, we use similarity and supervised learning models. Next, to discover de facto diagnosis specialties unlisted in the HPTCS, we introduce a general discovery‐evaluation framework. In this framework, we use a semi‐supervised learning model and an unsupervised learning model, from which the discovered specialties are subsequently evaluated by the similarity and supervised learning models used in recognition. To illustrate the potential for these approaches, we collect 2 data sets of 1 year of diagnosis histories from a large academic medical center: One is a subset of the other except for additional information useful for network analysis. The results indicate that 12 core de facto diagnosis specialties listed in the HPTCS are highly recognizable. Additionally, the semi‐supervised learning model discovers a specialty for breast cancer on the smaller data set based on network analysis, while the unsupervised learning model confirms this discovery and suggests an additional specialty for Obesity on the larger data set. The potential correctness of these 2 specialties is reinforced by the evaluation results that they are highly recognizable by similarity and supervised learning models in comparison with 12 core de facto diagnosis specialties listed in the HPTCS.
Learning Health Systems, 2018:e10057, 2018. DOI: 10.1002/lrh2.10057
Shubhra Kanti, Karmaker Santu, Vincent Bindschadler, ChengXiang Zhai, and Carl A. Gunter recently published a paper titled NRF: A Naive Re-identification Framework:
The promise of big data relies on the release and aggregation of data sets. When these data sets contain sensitive information about individuals, it has been scalable and convenient to protect the privacy of these individuals by de-identification. However, studies show that the combination of de-identified data sets with other data sets risks re-identification of some records. Some studies have shown how to measure this risk in specific contexts where certain types of public data sets (such as voter roles) are assumed to be available to attackers. To the extent that it can be accomplished, such analyses enable the threat of compromises to be balanced against the benefits of sharing data. For example, a study that might save lives by enabling medical research may be enabled in light of a sufficiently low probability of compromise from sharing de-identified data. In this paper, we introduce a general probabilistic re-identification framework that can be instantiated in specific contexts to estimate the probability of compromises based on explicit assumptions. We further propose a baseline of such assumptions that enable a first-cut estimate of risk for practical case studies. We refer to the framework with these assumptions as the Naive Re-identification Framework (NRF). As a case study, we show how we can apply NRF to analyze and quantify the risk of re-identification arising from releasing de-identified medical data in the context of publicly-available social media data. The results of this case study show that NRF can be used to obtain meaningful quantification of the re-identification risk, compare the risk of different social media, and assess risks of combinations of various demographic attributes and medical conditions that individuals may voluntarily disclose on social media.
ACM Workshop on Privacy in an Electronic Society (WPES ’18), Toronto, Canada, October 2018. DOI: 10.1145/3267323.3268948
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov recently published a paper titled Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations:
With the growing adoption of machine learning, sharing of learned models is becoming popular. However, in addition to the prediction properties the model producer aims to share, there is also a risk that the model consumer can infer other properties of the training data the model producer did not intend to share. In this paper, we focus on the inference of global properties of the training data, such as the environment in which the data was produced, or the fraction of the data that comes from a certain class, as applied to white-box Fully Connected Neural Networks (FCNNs). Because of their complexity and inscrutability, FCNNs have a particularly high risk of leaking unexpected information about their training sets; at the same time, this complexity makes extracting this information challenging. We develop techniques that reduce this complexity by noting that FCNNs are invariant under permutation of nodes in each layer. We develop our techniques using representations that capture this invariance and simplify the information extraction task. We evaluate our techniques on several synthetic and standard benchmark datasets and show that they are very effective at inferring various data properties. We also perform two case studies to demonstrate the impact of our attack. In the first case study we show that a classifier that recognizes smiling faces also leaks information about the relative attractiveness of the individuals in its training set. In the second case study we show that a classifier that recognizes Bitcoin mining from performance counters also leaks information about whether the classifier was trained on logs from machines that were patched for the Meltdown and Spectre attacks.
ACM Computer and Communications Security (CCS ’18), Toronto Canada, October 2018. DOI:10.1145/3243734.3243834
Juhee Kwon and Eric Johnson recently published an article aimed at the question Does “meaningful-use” attestation improve information security performance?
Certification mechanisms are often employed to assess and signal difficult-to-observe management practices and foster improvement. In the U.S. healthcare sector, a certification mechanism called meaningful-use attestation was recently adopted as part of an effort to encourage electronic health record (EHR) adoption while also focusing healthcare providers on protecting sensitive healthcare data. This new regime motivated us to examine how meaningful-use attestation influences the occurrence of data breaches. Using a propensity score matching technique combined with a difference-in-differences (DID) approach, our study shows that the impact of meaningful-use attestation is contingent on the nature of data breaches and the time frame. Hospitals that attest to having reached Stage 1 meaningful-use standards observe fewer external breaches in the short term, but do not see continued improvement in the following year. On the other hand, attesting hospitals observe short-term increases in accidental internal breaches but eventually see long-term reductions. We do not find any link between malicious internal breaches and attestation. Our findings offer theoretical and practical insights into the effective design of certification mechanisms.
The full paper appears in in MIS Quarterly. Vol. 42, No. 4 (December), 1043-1067, 2018. DOI: 10.25300/MISQ/2018/13580
THaW’s A.J. Burns and Eric Johnson recently published a piece in IT Professional:
Issue No. 03 – May./Jun. (2018 vol. 20)
Tim Pierson’s dissertation work resulted in an innovative method for single-antenna Wi-Fi devices (like many mHealth devices, medical devices, or those in the IoT) to determine with strong confidence whether a Wi-Fi transmitter is close by (within a few centimeters). This proximity detector can be the basis for trustworthy relationships between devices. A poster paper about this idea just won the best-poster award at MobiCom 2018, and the full paper was just accepted for presentation at MobiCom 2019. See below for the abstract, or check out the corresponding three-page paper.
At the Joint Conference on Pervasive and Ubiquitous Computing conference, Ubicomp, David Kotz presented THaW’s work to develop a novel biometric approach to identifying and verifying who is wearing a device – an important consideration for a medical device that may be collecting diagnostic information that is fed into an electronic health record. Their novel approach is to use vocal resonance, i.e., the sound of your voice as it passes through bones and tissues, for a device to recognize its wearer and verify that it is physically in contact with the wearer… not just nearby. They implemented the method on a wearable-class computing device and showed high accuracy and low energy consumption.
Rui Liu, Cory Cornelius, Reza Rawassizadeh, Ron Peterson, and David Kotz. Vocal Resonance: Using Internal Body Voice for Wearable Authentication. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (UbiComp), 2(1), March 2018. DOI 10.1145/3191751.
Abstract: We observe the advent of body-area networks of pervasive wearable devices, whether for health monitoring, personal assistance, entertainment, or home automation. For many devices, it is critical to identify the wearer, allowing sensor data to be properly labeled or personalized behavior to be properly achieved. In this paper we propose the use of vocal resonance, that is, the sound of the person’s voice as it travels through the person’s body – a method we anticipate would be suitable for devices worn on the head, neck, or chest. In this regard, we go well beyond the simple challenge of speaker recognition: we want to know who is wearing the device. We explore two machine-learning approaches that analyze voice samples from a small throat-mounted microphone and allow the device to determine whether (a) the speaker is indeed the expected person, and (b) the microphone-enabled device is physically on the speaker’s body. We collected data from 29 subjects, demonstrate the feasibility of a prototype, and show that our DNN method achieved balanced accuracy 0.914 for identification and 0.961 for verification by using an LSTM-based deep-learning model, while our efficient GMM method achieved balanced accuracy 0.875 for identification and 0.942 for verification.
In a recent Viewpoint article in JAMA, THaW member Kevin Fu explored a recent pacemaker vulnerability, and its ramifications for medical device security in general. In the post, he discusses both the full extent of the vulnerabilities, as well as the practical considerations to be taken as a result. To read the full text of the article, click the link below.
Cybersecurity Concerns and Medical Devices – Lessons From a Pacemaker Advisory