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Register a new userWidely Reused and Shared, Infrequently Updated, and Sometimes Inherited: A Holistic View of PIN Authentication in Digital Lives and Beyond
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Added by
Adam Aviv
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Personal Identification Numbers (PINs) are widely used as an access control mechanism for digital assets (e.g., smartphones), financial assets (e.g., ATM cards), and physical assets (e.g., locks for garage doors or homes). Using semi-structured interviews (n=35), participants reported on PIN usage for different types of assets, including how users choose, share, inherit, and reuse PINs, as well as behaviour following the compromise of a PIN. We find that memorability is the most important criterion when choosing a PIN, more so than security or concerns of reuse. Updating or changing a PIN is very uncommon, even when a PIN is compromised. Participants reported sharing PINs for one type of asset with acquaintances but inadvertently reused them for other assets, thereby subjecting themselves to potential risks. Participants also reported using PINs originally set by previous homeowners for physical devices (e.g., alarm or keypad door entry systems). While aware of the risks of not updating PINs, this did not always deter participants from using inherited PINs, as they were often missing instructions on how to update them. %While aware of the risks of not updating PINs, participants continued using these PINs, as they were often missing instructions on how to update them.Given the expected increase in PIN-protected assets (e.g., loyalty cards, smart locks, and web apps), we provide suggestions and future research directions to better support users with multiple digital and non-digital assets and more secure human-device interaction when utilizing PINs.
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Mobile device users avoiding observational attacks and coping with situational impairments may employ techniques for eyes-free mobile unlock authentication, where a user enters his/her passcode without looking at the device. This study supplies an initial description of user accu- racy in performing this authentication behavior with PIN and pattern passcodes, with varying lengths and visual characteristics. Additionally, we inquire if tactile-only feedback can provide assistive spatialization, finding that orientation cues prior to unlocking do not help. Measure- ments of edit distance and dynamic time warping accuracy were collected, using a within-group, randomized study of 26 participants. 1,021 passcode entry gestures were collected and classified, identifying six user strategies for using the pre-entry tactile feedback, and ten codes for types of events and errors that occurred during entry. We found that users who focused on orienting themselves to position the first digit of the passcode using the tactile feedback performed better in the task. These results could be applied to better define eyes-free behavior in further research, and to design better and more secure methods for eyes-free authentication.
In encryption, non-malleability is a highly desirable property: it ensures that adversaries cannot manipulate the plaintext by acting on the ciphertext. Ambainis, Bouda and Winter gave a definition of non-malleability for the encryption of quantum data. In this work, we show that this definition is too weak, as it allows adversaries to inject plaintexts of their choice into the ciphertext. We give a new definition of quantum non-malleability which resolves this problem. Our definition is expressed in terms of entropic quantities, considers stronger adversaries, and does not assume secrecy. Rather, we prove that quantum non-malleability implies secrecy; this is in stark contrast to the classical setting, where the two properties are completely independent. For unitary schemes, our notion of non-malleability is equivalent to encryption with a two-design (and hence also to the definition of Ambainis et al.). Our techniques also yield new results regarding the closely-related task of quantum authentication. We show that total authentication (a notion recently proposed by Garg, Yuen and Zhandry) can be satisfied with two-designs, a significant improvement over the eight-design construction of Garg et al. We also show that, under a mild adaptation of the rejection procedure, both total authentication and our notion of non-malleability yield quantum authentication as defined by Dupuis, Nielsen and Salvail.
With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for real-time data processing and analysis has emerged as an essential pre-requite for customers satisfaction. In this direction, Mobile Edge Computing (MEC) provides seamless services with reduced latency, enhanced mobility, and improved location awareness. Since MEC has evolved from Cloud Computing, it inherited numerous security and privacy issues from the latter. Further, decentralized architectures and diversified deployment environments used in MEC platforms also aggravate the problem; causing great concerns for the research fraternity. Thus, in this paper, we propose an efficient and lightweight mutual authentication protocol for MEC environments; based on Elliptic Curve Cryptography (ECC), one-way hash functions and concatenation operations. The designed protocol also leverages the advantages of discrete logarithm problems, computational Diffie-Hellman, random numbers and time-stamps to resist various attacks namely-impersonation attacks, replay attacks, man-in-the-middle attacks, etc. The paper also presents a comparative assessment of the proposed scheme relative to the current state-of-the-art schemes. The obtained results demonstrate that the proposed scheme incurs relatively less communication and computational overheads, and is appropriate to be adopted in resource constraint MEC environments.
Todays large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the de-facto regulators of our societies information diet, from shaping opinions on public health to organizing groups for social movements. This creates serious concerns, but also great opportunities to promote quality information. Addressing the concerns and seizing the opportunities is a challenging, enormous and fabulous endeavor, as intuitively appealing ideas often come with unwanted {it side effects}, and as it requires us to think about what we deeply prefer. Understanding how todays large-scale algorithms are built is critical to determine what interventions will be most effective. Given that these algorithms rely heavily on {it machine learning}, we make the following key observation: emph{any algorithm trained on uncontrolled data must not be trusted}. Indeed, a malicious entity could take control over the data, poison it with dangerously manipulative fabricated inputs, and thereby make the trained algorithm extremely unsafe. We thus argue that the first step towards safe and ethical large-scale algorithms must be the collection of a large, secure and trustworthy dataset of reliable human judgments. To achieve this, we introduce emph{Tournesol}, an open source platform available at url{https://tournesol.app}. Tournesol aims to collect a large database of human judgments on what algorithms ought to widely recommend (and what they ought to stop widely recommending). We outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project. Most importantly, we argue that, if successful, Tournesol may then serve as the essential foundation for any safe and ethical large-scale algorithm.
Today, there is a plethora of software security tools employing visualizations that enable the creation of useful and effective interactive security analyst dashboards. Such dashboards can assist the analyst to understand the data at hand and, consequently, to conceive more targeted preemption and mitigation security strategies. Despite the recent advances, model-based security analysis is lacking tools that employ effective dashboards---to manage potential attack vectors, system components, and requirements. This problem is further exacerbated because model-based security analysis produces significantly larger result spaces than security analysis applied to realized systems---where platform specific information, softwar
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