Human errors in performing security-critical tasks are typically blamed on the complexity of those tasks. However, such errors can also occur because of (possibly unexpected) sensory distractions. A sensory distraction that produces negative effects can be abused by the adversary that controls the environment. Meanwhile, a distraction with positive effects can be artificially introduced to improve user performance. The goal of this work is to explore the effects of visual stimuli on the performance of security-critical tasks. To this end, we experimented with a large number of subjects who were exposed to a range of unexpected visual stimuli while attempting to perform Bluetooth Pairing. Our results clearly demonstrate substantially increased task completion times and markedly lower task success rates. These negative effects are noteworthy, especially, when contrasted with prior results on audio distractions which had positive effects on performance of similar tasks. Experiments were conducted in a novel (fully automated and completely unattended) experimental environment. This yielded more uniform experiments, better scalability and significantly lower financial and logistical burdens. We discuss this experience, including benefits and limitations of the unattended automated experiment paradigm.
@cite_1 discovered that subject's performance in secure device pairing could be improved if it were to be scored. In other words, notifying subjects about their performance score resulted in fewer errors.
@cite_1
15257636 2148165512 1830858207 2019258915 1483712199 2024051019 2106044541 2096768518 2108078432 1779849045 2042286431 2031481606 2177315755 1964842920 2151905466 2120034667 2149753900 335129981 2963940534 2124183676
We explore the use of extrinsic motivation to improve the state of user-centered security mechanisms. Specifically, we study applications of scores as user incentives in the context of secure device pairing. We develop a scoring functionality that can be integrated with traditional pairing approaches. We then report on a usability study that we performed to evaluate the effect of scoring on the performance of users in comparison operations. Our results demonstrate that individuals are likely to commit fewer errors and show more acceptance when working with the scoring based pairing approach. Framing pairing as a game and providing feedback to users in the form of a score is an efficient way to improve pairing security, particularly among users such as children who may not be aware of the consequences of their decisions while performing security tasks. Secure Device Pairing is the bootstrapping of secure communication between two previously unassociated devices over a wireless channel. The human-imperceptible nature of wireless communication, lack of any prior security context, and absence of a common trust infrastructure open the door for Man-in-the-Middle (aka Evil Twin) attacks. A number of methods have been proposed to mitigate these attacks, each requiring user assistance in authenticating information exchanged over the wireless channel via some human-perceptible auxiliary channels, e.g., visual, acoustic or tactile. In this paper, we present results of the first comprehensive and comparative study of eleven notable secure device pairing methods. Usability measures include: task performance times, ratings on System Usability Scale (SUS), task completion rates, and perceived security. Study subjects were controlled for age, gender and prior experience with device pairing. We present overall results and identify problematic methods for certain classes of users as well as methods best-suited for various device configurations. Author(s): Kaczmarek, T; Kobsa, A; Sy, R; Tsudik, G | Abstract: User errors while performing security-critical tasks can lead to undesirable or even disastrous consequences. One major factor influencing mistakes and failures is complexity of such tasks, which has been studied extensively in prior research. Another important issue which hardly received any attention is the impact of both accidental and intended distractions on users performing security-critical tasks. In particular, it is unclear whether, and to what extent, unexpected sensory cues (e.g., auditory or visual) can influence user behavior and or trigger mistakes. Better understanding of the effects of intended distractions will help clarify their role in adversarial models. As part of the research effort described in this paper, we administered a range of naturally occurring -- yet unexpected -- sounds while study participants attempted to perform a security-critical task. We found that, although these auditory cues lowered participants' failure rates, they had no discernible effect on their task completion times. To this end, we overview some relevant literature that explains these somewhat counter-intuitive findings. Conducting a thorough and meaningful study on user errors requires a large number of participants, since errors are typically infrequent and should not be instigated more than once per subject. To reduce the effort of running numerous subjects, we developed a novel experimental setup that was fully automated and unattended. We discuss our experience with this setup and highlight the pros and cons of generalizing its usage. Forming secure associations between wireless devices that do not share a prior trust relationship is an important problem. This paper presents ProxiMate, a system that allows wireless devices in proximity to securely pair with one another autonomously by generating a common cryptographic key directly from their shared time-varying wireless environment. The shared key synthesized by ProxiMate can be used by the devices to authenticate each others' physical proximity and then to communicate confidentially. Unlike traditional pairing approaches such as Diffie-Hellman, ProxiMate is secure against a computationally unbounded adversary and its computational complexity is linear in the size of the key. We evaluate ProxiMate using an experimental prototype built using an open-source software-defined platform and demonstrate its effectiveness in generating common secret bits. We further show that it is possible to speed up secret key synthesis by monitoring multiple RF sources simultaneously or by shaking together the devices that need to be paired. Finally, we show that ProxiMate is resistant to even the most powerful attacker who controls the public RF source used by the legitimate devices for pairing. The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94 in EER is observed on the FVC2002 DB3 database. In this paper, we propose a rank minimization method to fuse the predicted confidence scores of multiple models, each of which is obtained based on a certain kind of feature. Specifically, we convert each confidence score vector obtained from one model into a pairwise relationship matrix, in which each entry characterizes the comparative relationship of scores of two test samples. Our hypothesis is that the relative score relations are consistent among component models up to certain sparse deviations, despite the large variations that may exist in the absolute values of the raw scores. Then we formulate the score fusion problem as seeking a shared rank-2 pairwise relationship matrix based on which each original score matrix from individual model can be decomposed into the common rank-2 matrix and sparse deviation errors. A robust score vector is then extracted to fit the recovered low rank score relation matrix. We formulate the problem as a nuclear norm and l 1 norm optimization objective function and employ the Augmented Lagrange Multiplier (ALM) method for the optimization. Our method is isotonic (i.e., scale invariant) to the numeric scales of the scores originated from different models. We experimentally show that the proposed method achieves significant performance gains on various tasks including object categorization and video event detection. Secure pairing of electronic devices is an important issue that must be addressed in many contexts. In the absence of prior security context, the need to involve the user in the pairing process is a prominent challenge. In this paper, we investigate the use of the audio channel for human-assisted device pairing. First we assume a common (insecure) wireless channel between devices. We then obviate the assumption of a pre-existing common channel with a single-channel device pairing approach only based on audio. Both approaches are applicable to a wide range of devices and place light burden on the user. A fairly common modern setting entails users, each in possession of a personal wireless device, wanting to communicate securely, via their devices. If these users (and their devices) have no prior association, a new security context must be established. In order to prevent potential attacks, the initial context (association) establishment process must involve only the intended devices and their users. A number of methods for initial secure association of two devices have been proposed; their usability factors have been explored and compared extensively. However, a more challenging problem of initial secure association of a group of devices (and users) has not received much attention. Although a few secure group association methods have been proposed, their usability aspects have not been studied, especially, in a comparative manner. This paper discusses desirable features and evaluation criteria for secure group association, identifies suitable methods and presents a comparative usability study. Results show that some simple methods (e.g., peer- or leader-based number comparisons) are quite attractive for small groups, being fast, reasonably secure and well-received by users. In this paper we bound the reliability function of decoding with errors and erasures for a streaming multiple-access channel with feedback. We show that, subject to an arbitrarily small bound on the probability of erasure, the best known lower bound on the reliability function (i.e., achievable error exponent) for the single-user version of our problem can also be achieved in the multi-user setting for high sum-rates. In other words, at high rates the interference of another user need not decrease the achievable error exponent of either. While general secure function evaluation (SFE) with information-theoretical (IT) security is infeasible in presence of a corrupted majority in the standard model, there are SFE protocols ( [STOC'87]) that are computationally secure (without fairness) in presence of an actively corrupted majority of the participants. Now, computational assumptions can usually be well justified at the time of protocol execution. The concern is rather a potential violation of the privacy of sensitive data by an attacker whose power increases over time. Therefore, we ask which functions can be computed with long-term security, where we admit computational assumptions for the duration of a computation, but require IT security (privacy) once the computation is concluded. Towards a combinatorial characterization of this class of functions, we also characterize the classes of functions that can be computed IT securely in the authenticated channels model in presence of passive, semi-honest, active, and quantum adversaries. Distributed detection on the serial (or tandem) topology is considered with the probability of error performance criterion. Previously published efforts, while presenting probability of detection versus false alarm results, limited the number of array elements to two or three. For the detection of known, equally likely signals in additive, symmetric noise, the author presents simple recursive expressions for the threshold values and the performance of the system. Examples for known signals in Gaussian and Laplacian noise show the degradation in performance due to the array structure. > Solutions for pairing devices without prior security associations typically require users to actively take part in the pairing process of the devices. Scenarios involving new types of devices like Internet-of-Things (IoT) appliances and wearable devices make it, however, desirable to be able to pair users' personal devices without user involvement. In this paper, we present a new approach for secure zero-interaction pairing suitable for IoT and wearable devices. We primarily require pairing to happen between "correct" devices -- the devices that the user intends to pair. Our pairing scheme identifies the correct devices based on measuring sustained co-presence over time. We do this by having the devices compute a fingerprint of their ambient context using information gathered through commonly available sensor modalities like ambient noise and luminosity. We introduce a novel robust and inexpensive approach for fingerprinting contexts over time. Co-present devices will observe roughly similar context fingerprints that we use in a key evolution protocol to gradually increase the confidence in the authenticity of the correct devices. Our experiments show the effectiveness of this approach for zero-interaction pairing. How accurately are people able to use the absolute category rating (ACR) 5-level scale? Put another way, how repeatable are an individual subject’s scores? Several subjective experiments have asked subjects to rate the same sequences a couple of times. Analyses indicate that none of the subjects exactly repeated their prior scores for these sequences. We would like to better understand this imperfection. This paper uses ACR subjective video quality tests to explore the precision of subjective ratings. To make formal measurements possible, we propose a theoretical subject model that is the main contribution of this paper. The proposed subject model indicates three major factors that influence accuracy: subject bias, subject inaccuracy, and stimulus scoring difficulty. These appear to be separate random effects and their existence is a reason why none of the subjects were able to perfectly repeat scores. There are three key consequences. First, subject scoring behavior includes a random component that spans approximately half of the rating scale. Second, the sensitivity and accuracy of most subjective analyses can be improved if the subject scores are normalized by removing subject bias. Third, to some extent, multiple subjects can be replaced with a single subject who rates each sequence multiple times. The problem of binary hypothesis testing is considered in a bandwidth-constrained densely populated low-power wireless sensor network operating over insecure links. Observations of the sensors are quantized and encrypted before transmission. The encryption method maps the output of the quantizer to one of the possible quantizer output levels randomly according to a probability matrix. The intended (ally) fusion center (AFC) is aware of the encryption keys (probabilities) while the unauthorized (third party) fusion center (TPFC) is not. A constrained optimization problem is formulated from the point of view of AFC in order to design its decision rule along with the encryption probabilities. The objective function to be minimized is the error probability of AFC and the constraint is a lower bound on the error probability of TPFC. In the binary case the optimal solution is found and in the nonbinary case a good suboptimal solution is analytically obtained. Numerical results are presented to show that it is possible to degrade the error probability of TPFC significantly and still achieve very low probability of error for AFC. The proposed method which may be considered a PHY-layer security scheme is highly scalable since it does not increase the packet overhead or transmit power of the sensors and has very low computational complexity. A scheme is described to randomize the keys so as to defeat any key space exploration attack. This is a study of the security of the Coherent One-Way (COW) protocol for quantumcryptography, proposed recently as a simple and fast experimental scheme. In thezero-error regime, the eavesdropper Eve can only take advantage of the losses in thetransmission. We consider new attacks, based on unambiguous state discrimination,which perform better than the basic beam-splitting attack, but which can be detectedby a careful analysis of the detection statistics. These results stress the importance oftesting several statistical parameters in order to achieve higher rates of secret bits. We consider sequential decision problems under uncertainty, where we seek to optimize an unknown function from noisy samples. This requires balancing exploration (learning about the objective) and exploitation (localizing the maximum), a problem well-studied in the multiarmed bandit literature. In many applications, however, we require that the sampled function values exceed some prespecified "safety" threshold, a requirement that existing algorithms fail to meet. Examples include medical applications where patient comfort must be guaranteed, recommender systems aiming to avoid user dissatisfaction, and robotic control, where one seeks to avoid controls causing physical harm to the platform. We tackle this novel, yet rich, set of problems under the assumption that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop an efficient algorithm called SAFEOPT, and theoretically guarantee its convergence to a natural notion of optimum reachable under safety constraints. We evaluate SAFEOPT on synthetic data, as well as two real applications: movie recommendation, and therapeutic spinal cord stimulation. Face recognition technologies have seen dramatic improvements in performance over the past decade, and such systems are now widely used for security and commercial applications. Since recognizing faces is a task that humans are understood to be very good at, it is common to want to compare automatic face recognition (AFR) and human face recognition (HFR) in terms of biometric performance. This paper addresses this question by: 1) conducting verification tests on volunteers (HFR) and commercial AFR systems and 2) developing statistical methods to support comparison of the performance of different biometric systems. HFR was tested by presenting face-image pairs and asking subjects to classify them on a scale of ldquoSame,rdquo ldquoProbably Same,rdquo ldquoNot sure,rdquo ldquoProbably Different,rdquo and ldquoDifferentrdquo; the same image pairs were presented to AFR systems, and the biometric match score was measured. To evaluate these results, two new statistical evaluation techniques are developed. The first is a new way to normalize match-score distributions, where a normalized match score is calculated as a function of the angle from a representation of [false match rate, false nonmatch rate] values in polar coordinates from some center. Using this normalization, we develop a second methodology to calculate an average detection error tradeoff (DET) curve and show that this method is equivalent to direct averaging of DET data along each angle from the center. This procedure is then applied to compare the performance of the best AFR algorithms available to us in the years 1999, 2001, 2003, 2005, and 2006, in comparison to human scores. Results show that algorithms have dramatically improved in performance over that time. In comparison to the performance of the best AFR system of 2006, 29.2 of human subjects performed better, while 37.5 performed worse. We propose a framework for secure device-pairing based on contextual information. In contrast to related work, we utilise fuzzy cryptography schemes to establish a common secret among devices and to account for noise in the contextual features. The framework is implemented for ambient audio and its feasibility is demonstrated in a case study. Additionally, we discuss properties of fingerprints from ambient audio and estimate the entropy of fingerprints in statistical tests. In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate this for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10^ -4 ) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F1-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we present network architectures that perform superior as well as produce results with higher stability on unseen data. In secure multiparty computation, mutually distrusting users in a network want to collaborate to compute functions of data which is distributed among the users. The users should not learn any additional information about the data of others than what they may infer from their own data and the functions they are computing. Previous works have mostly considered the worst case context (i.e., without assuming any distribution for the data); Lee and Abbe (2014) is a notable exception. Here, we study the average case (i.e., we work with a distribution on the data) where correctness and privacy is only desired asymptotically. For concreteness and simplicity, we consider a secure version of the function computation problem of K "orner and Marton (1979) where two users observe a doubly symmetric binary source with parameter p and the third user wants to compute the XOR. We show that the amount of communication and randomness resources required depends on the level of correctness desired. When zero-error and perfect privacy are required, the results of (2014) show that it can be achieved if and only if a total rate of 1 bit is communicated between every pair of users and private randomness at the rate of 1 is used up. In contrast, we show here that, if we only want the probability of error to vanish asymptotically in block length, it can be achieved by a lower rate (binary entropy of p) for all the links and for private randomness; this also guarantees perfect privacy. We also show that no smaller rates are possible even if privacy is only required asymptotically.