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_6 examined usability of device pairing in a group setting. In this setting, up to 6 users tried to connect their devices to one another by participating in a SAS protocol. It was found that group effort decreased the expected rate of security and non-security failures. However, if a single individual was shown a SAS different from that of all others participants, the former often lied about the SAS in order to fit in with the group, demonstrating so-called insecurity of conformity.''
@cite_6
2144454907
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. A comparison of two methods for surmounting the multiple-minima problem, Simulated Annealing (SA) and Monte Carlo with Minimization (MCM), is presented with applications to [Met]-enkephalin in the absence and in the presence of water. SA explores a continuous space of internal variables, while MCM explores a discrete space consisting of the local energy minima on that space. Starting from random conformations chosen from the whole conformational space in both cases, it is found that, while SA converges to low-energy structures significantly faster than MCM, the former does not converge to a unique minimum whereas the latter does. Furthermore, the behavior of the RMS deviations with respect to the apparent global minimum (for enkephalin in the absence of water) shows no correlation with the observed overall energy decrease in the case of SA, whereas such a correlation is quite evident with MCM; this implies that, even though the potential energy decreases in the annealing process, the Monte Carlo SA trajectory does not proceed towards the global minimum. Possible reasons for these differences between the two methods are discussed. It is concluded that, while SA presents attractive prospects for possibly improving or refining given structures, it must be considered inferior to MCM, at least in problems where little or no structural information is available for the molecule of interest. Key agreement protocols are frequently based on the Diffie-Hellman protocol but require authenticating the protocol messages in two ways. This can be made by a cross-authentication protocol. Such protocols, based on the assumption that a channel which can authenticate short strings is available (SAS-based), have been proposed by Vaudenay. In this paper, we survey existing protocols and we propose a new one. Our proposed protocol requires three moves and a single SAS to be authenticated in two ways. It is provably secure in the random oracle model. We can further achieve security with a generic construction (e.g. in the standard model) at the price of an extra move. We discuss applications such as secure peer-to-peer VoIP. Today, the publication of microdata poses a privacy threat: anonymous personal records can be re-identified using third data sources. Past research has tried to develop a concept of privacy guarantee that an anonymized data set should satisfy before publication, culminating in the notion of t-closeness. To satisfy t-closeness, the records in a data set need to be grouped into Equivalence Classes (ECs), such that each EC contains records of indistinguishable quasi-identifier values, and its local distribution of sensitive attribute (SA) values conforms to the global table distribution of SA values. However, despite this progress, previous research has not offered an anonymization algorithm tailored for t-closeness. In this paper, we cover this gap with SABRE, a SA Bucketization and REdistribution framework for t-closeness. SABRE first greedily partitions a table into buckets of similar SA values and then redistributes the tuples of each bucket into dynamically determined ECs. This approach is facilitated by a property of the Earth Mover's Distance (EMD) that we employ as a measure of distribution closeness: If the tuples in an EC are picked proportionally to the sizes of the buckets they hail from, then the EMD of that EC is tightly upper-bounded using localized upper bounds derived for each bucket. We prove that if the t-closeness constraint is properly obeyed during partitioning, then it is obeyed by the derived ECs too. We develop two instantiations of SABRE and extend it to a streaming environment. Our extensive experimental evaluation demonstrates that SABRE achieves information quality superior to schemes that merely applied algorithms tailored for other models to t-closeness, and can be much faster as well. Sentiment Analysis (SA) is the process of determining the sentiment of a text written in a natural language to be positive, negative or neutral. It is one of the most interesting subfields of natural language processing (NLP) and Web mining due to its diverse applications and the challenges associated with applying it on the massive amounts of textual data available online (especially, on social networks). Most of the current works on SA focus on the English language and follow one of two main approaches, (corpus-based and lexicon-based) or a hybrid of them. This work focuses on a less studied aspect of SA, which is lexicon-based SA for the Arabic language. In addition to experimenting and comparing three different lexicon construction techniques, an Arabic SA tool is designed and implemented to effectively take advantage of the constructed lexicons. The proposed SA tool possesses many novel features such as the way negation and intensification are handled. The experimental results show encouraging outcomes with 74.6 accuracy in addition to revealing new insights and guidelines that could direct the future research efforts. Distributed-memory parallel supercomputers are an important platform for the execution of high-performance parallel jobs. In order to submit a job for execution in most supercomputers, one has to specify the number of processors to be allocated to the job. However, most parallel jobs in production today are moldable. A job is moldable when the number of processors it needs to execute can vary, although such a number has to be fixed before the job starts executing. Consequently, users have to decide how many processors to request whenever they submit a moldable job. In this dissertation, we show that the request that submits a moldable job can be automatically selected in a way that often reduces the job's turn-around time. The turn-around time of a job is the time elapsed between the job's submission and its completion. More precisely, we will introduce and evaluate SA, an application scheduler that chooses which request to use to submit a moldable job on behalf of the user. The user provides SA with a set of possible requests that can be used to submit a given moldable job. SA estimates the turn-around time of each request based on the current state of the supercomputer, and then forwards to the supercomputer the request with the smallest expected turn-around time. Users are thus relieved by SA of a task unrelated with their final goals, namely that of selecting which request to use. Moreover and more importantly, SA often improves the turn-around time of the job under a variety of conditions. The conditions under which SA was studied cover variations on the characteristics of the job, the state of the supercomputer, and the information available to SA. The emergent behavior generated by having most jobs using SA to craft their requests was also investigated. The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Graph clustering techniques are very useful for detecting densely connected groups in a large graph. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties which are often heterogenous. In this paper, we propose a novel graph clustering algorithm, SA-Cluster, based on both structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparison with the state-of-the-art graph clustering and summarization methods. This chapter reviews the principles of sample average approximation (SAA) for solving simulation optimization problems. We provide an accessible overview of the area and survey interesting recent developments. We explain when one might want to use SAA and when one might expect it to provide good-quality solutions. We also review some of the key theoretical properties of the solutions obtained through SAA. We contrast SAA with stochastic approximation (SA) methods in terms of the computational effort required to obtain solutions of a given quality, explaining why SA “wins” asymptotically. However, an extension of SAA known as retrospective optimization can match the asymptotic convergence rate of SA, at least up to a multiplicative constant. The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the distributed protocol that maintains the blockchain, run by participants called miners. Conventional wisdom asserts that the mining protocol is incentive-compatible and secure against colluding minority groups, that is, it incentivizes miners to follow the protocol as prescribed. We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email 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. This paper describes an experiment to investigate user perceptions of the usability and security of single-factor and two-factor authentication methods in automated telephone banking. In a controlled experiment with 62 banking customers a knowledge-based, single-factor authentication procedure, based on those commonly used in the financial services industry, was compared with a two-factor approach where in addition to the knowledge-based step, a one-time passcode was generated using a hardware security token. Results were gathered on the usability and perceived security of the two methods described, together with call completion rates and call durations for the two methods. Significant differences were found between the two methods, with the two-factor version being perceived as offering higher levels of security than the single-factor authentication version; however, this gain was offset by significantly lower perceptions of usability, and lower ratings for convenience and ease of use for the two-factor version. In addition, the two-factor authentication version took longer for participants to complete. This research provides valuable empirical evidence of the trade-off between security and usability in automated systems. Users often struggle to create passwords under strict requirements. To make this process easier, some providers present real-time feedback during password creation, indicating which requirements are not yet met. Other providers guide users through a multi-step password-creation process. Our 6,435-participant online study examines how feedback and guidance affect password security and usability. We find that real-time password-creation feedback can help users create strong passwords with fewer errors. We also find that although guiding participants through a three-step password-creation process can make creation easier, it may result in weaker passwords. Our results suggest that service providers should present password requirements with feedback to increase usability. However, the presentation of feedback and guidance must be carefully considered, since identical requirements can have different security and usability effects depending on presentation. Mobile social apps provide sharing and networking opportunities based on a user's location, activity, and set of nearby users. A platform for these apps must meet a wide range of communication needs while ensuring users' control over their privacy. In this paper, we introduce EnCore, a mobile platform that builds on secure encounters between pairs of devices as a foundation for privacy-preserving communication. An encounter occurs whenever two devices are within Bluetooth radio range of each other, and generates a unique encounter ID and associated shared key. EnCore detects nearby users and resources, bootstraps named communication abstractions called events for groups of proximal users, and enables communication and sharing among event participants, while relying on existing network, storage and online social network services. At the same time, EnCore puts users in control of their privacy and the confidentiality of the information they share. Using an Android implementation of EnCore and an app for event-based communication and sharing, we evaluate EnCore's utility using a live testbed deployment with 35 users. Secret key generation among wireless devices using physical layer information of radio channel has been an attractive alternative for ensuring security in mobile environments. Received signal strength (RSS) based secret key extraction gains much attention due to its easy accessibility in wireless infrastructure. However, the problem of using RSS to generate keys among multiple devices to ensure secure group communication in practice remains open. In this work, we propose a framework for collaborative key generation among multiple wireless devices leveraging RSS. To deal with mobile devices not within each other’s communication range, we employ relay nodes to achieve reliable key extraction. To enable secure group communication, two protocols are developed to perform collaborative group key generation via star and chain topologies respectively. We further provide the theoretic analysis on the achievable secrecy rate for both star and chain topologies in the presence of an eavesdropper. Our prototype development using MICAz motes and extensive experiments using fading trend based key extraction demonstrate the feasibility of using RSS for group key generation in both indoor and outdoor environments, and concurrently achieving a lower bit mismatch rate compared to existing studies. Strong authentication for online service access typically requires some kind of hardware device for generating dynamic access credentials that are often used in combination with static passwords. This practice have the side effect that users fill up their pockets with more and more devices and their heads with more and more passwords. This situation becomes increasinlgy difficult to manage which in turn degrades the usability of online services. In order to cope with this situation users often adopt insecure ad hoc practices that enable them to practically manage their different identities and credentials. This paper explores how one single device can be used for authentication of user to service providers and server to users, as well as provide a range of other security services. In the museum visitor routing problem (MVRP), each visitor group has some exhibit rooms of interest. The visiting route of a certain visitor group requires going through all the exhibit rooms that the group wants to visit. Routes need to be scheduled based on certain criteria to avoid congestion and or prolonged touring time. In this study, the MVRP is formulated as a mixed integer program which is an extension of the open shop scheduling (OSS) problem in which visitor groups and exhibit rooms are treated as jobs and machines, respectively. The time each visitor group spends in an exhibit room is analogous to the processing time required for each job on a particular machine. The travel time required from one exhibit room to another is modeled as the sequence-dependent setup time on a machine, which is not considered in the OSS problem. Due to the intrinsic complexity of the MVRP, a simulated annealing (SA) approach is proposed to solve the problem. Computational results indicate that the proposed SA approach is capable of obtaining high quality MVRP solutions within a reasonable amount of computational time and enables the approach to be used in practice. In this paper we demonstrate that it is possible to help individuals regulate their emotions with mobile interventions that leverage the way we naturally react to our bodily signals. Previous studies demonstrate that the awareness of our bodily signals, such as our heart rate, directly influences the way we feel. By leveraging these findings we designed a wearable device to regulate user's anxiety by providing a false feedback of a slow heart rate. The results of an experiment with 67 participants show that the device kept the anxiety of the individuals in low levels when compared to the control group and the other conditions. We discuss the implications of our findings and present some promising directions for designing and developing this type of intervention for emotion regulation. 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. Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a "black box" operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS INSIGHT's Interactive Data Exploration. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants' accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.