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