Towards Touch-to-Access Device Authentication Using Induced Body Electric Potentials

This paper presents TouchAuth, a new touch-to-access device authentication approach using induced body electric potentials (iBEPs) caused by the indoor ambient electric field that is mainly emitted from the building's electrical cabling. The design of TouchAuth is based on the electrostatics of iBEP generation and a resulting property, i.e., the iBEPs at two close locations on the same human body are similar, whereas those from different human bodies are distinct. Extensive experiments verify the above property and show that TouchAuth achieves high-profile receiver operating characteristics in implementing the touch-to-access policy. Our experiments also show that a range of possible interfering sources including appliances' electromagnetic emanations and noise injections into the power network do not affect the performance of TouchAuth. A key advantage of TouchAuth is that the iBEP sensing requires a simple analog-to-digital converter only, which is widely available on microcontrollers. Compared with existing approaches including intra-body communication and physiological sensing, TouchAuth is a low-cost, lightweight, and convenient approach for authorized users to access the smart objects found in indoor environments.Comment: 16 pages, accepted to the 25th Annual International Conference on Mobile Computing and Networking (MobiCom 2019), October 21-25, 2019, Los Cabos, Mexic

CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to ``Contrastive Language-Image Pre-training (CLIP)'' and ``Locked-image Tuning (LiT)'' that have recently gained considerable attention. Most existing works for cross-modal representation alignment (including CLIP and LiT) use the standard contrastive training objective, which employs sets of positive and negative examples to align similar and repel dissimilar training data samples. However, similarity amongst training examples has a more continuous nature, thus calling for a more `non-binary' treatment. To address this, we propose a novel loss function called Continuously Weighted Contrastive Loss (CWCL) that employs a continuous measure of similarity. With CWCL, we seek to align the embedding space of one modality with another. Owing to the continuous nature of similarity in the proposed loss function, these models outperform existing methods for 0-shot transfer across multiple models, datasets and modalities. Particularly, we consider the modality pairs of image-text and speech-text and our models achieve 5-8% (absolute) improvement over previous state-of-the-art methods in 0-shot image classification and 20-30% (absolute) improvement in 0-shot speech-to-intent classification and keyword classification.Comment: Accepted to Neural Information Processing Systems (NeurIPS) 2023 conferenc

Optimizable Design Schemes in Communication Systems for Providing Anonymity and Confidentiality

Thesis (Ph.D.)--University of Washington, 2015In applications requiring anonymity such as electronic mail, evading website censorship, and file sharing, the identities of source-destination pairs for each data flow should be untraceable. To achieve this goal, anonymous networks use covert relays to prevent unauthorized entities from determining communicating parties through traffic timing analysis. In a multipath anonymous network, the choice of which relay nodes should be covert, as well as the route selection by the network nodes, affect both the anonymity and network performance. Although assigning relays as covert and selecting routes composed of covert relays can provide higher anonymity, the selection of these two parameters will increase the packet dropping rate of the network. Therefore, how to choose relays as covert and how to select routes among multiple paths should be studied. In this thesis, we present analytical frameworks for relay assignment and route selection in multi-path anonymous wireless networks. We show that joint relay assignment and route selection can be formulated as a convex optimization problem which guarantees global optimum solution. Our frameworks also consider two special cases. The first case is the route selection alone by giving the relay configuration. The second case is choosing relays as covert or not given the route selection strategies. Given a subset of nodes is chosen to act as covert relays to hide timing information from unauthorized observers. We propose route selection methods that maximize anonymity for multipath wireless networks with predetermined covert relay nodes, while taking into account packet loss as a constraint. Using a rate-distortion framework, we show how to assign probabilities which split the flows from source to destination among all possible routes and show that selecting routes according to the assigned probabilities achieves maximum anonymity given the packet-loss constraint. When sources and destinations are independent to each other, each source allocates route independently as well. When sources destination pairs are dependent, we investigate how to allocate routes for each source-destination pair to maximize anonymity with packet-loss rate as a constraint. Since each source may have incomplete knowledge of which destination and routes other sources choose due to packet encryption and radio range, we consider three different cases depending on each source knowledge of other sources. In each case, we show how to split flows among multiple paths to maximize anonymity under packet-loss constraint by considering the optimization as a rate-distortion problem. The optimal relay configuration for given fixed route section can be derived from the information theoretic anonymity metric of joint relay and route selection. We showed that the problem of optimal relay assignment based on the trade-off between anonymity and throughput in a multiple wireless network can be solved by re-deriving rate-distorting frameworks. Our framework guarantees efficient computation of the global optimum. For RFID system, to ensure security and privacy of passive RFID have notoriously been a difficult problem. In a passive RFID system, each tag has power and computational constraints. As a result, providing privacy protection can be a very challenging task. In this thesis, we propose a physical layer privacy protection scheme termed Varying Reader’s Transmitted Amplitude (VRTA). This scheme provides the data confidentiality in the uplink direction. i.e., tag to reader. By doing so, the user’s privacy is assured against the passive eavesdroppers. This scheme requires no modifications on the tag and the existing protocols as well as no pre-shared secrets. It has minimal impact on the existing system. We also perform analysis to show our scheme is theoretically secure against one and multiple passive eavesdroppers with properly chosen system parameters. Then, by applying VRTA system, the RFID tag can establish key agreement with reader for encryption or authentication purpose. Finally, we implement our scheme using USRP to validate our theoretical results and to further test the performance of our proposed scheme