We introduce Pixie, a novel, camera based two factor authentication solution for mobile and wearable devices. A quick and familiar user action of snapping a photo is sufficient for Pixie to simultaneously perform a graphical password authentication and a physical token based authentication, yet it does not require any expensive, uncommon hardware. Pixie establishes trust based on both the knowledge and possession of an arbitrary physical object readily accessible to the user, called trinket. Users choose their trinkets similar to setting a password, and authenticate by presenting the same trinket to the camera. The fact that the object is the trinket, is secret to the user. Pixie extracts robust, novel features from trinket images, and leverages a supervised learning classifier to effectively address inconsistencies between images of the same trinket captured in different circumstances. Pixie achieved a false accept rate below 0.09 in a brute force attack with 14.3 million authentication attempts, generated with 40,000 trinket images that we captured and collected from public datasets. We identify master images, that match multiple trinkets, and study techniques to reduce their impact. In a user study with 42 participants over 8 days in 3 sessions we found that Pixie outperforms text based passwords on memorability, speed, and user preference. Furthermore, Pixie was easily discoverable by new users and accurate under field use. Users were able to remember their trinkets 2 and 7 days after registering them, without any practice between the 3 test dates.
Table compares the user entry times of Pixie with various other authentication solutions. While Pixie takes longer than biometric authentication based on face @cite_45 , it is still faster than several authentication solutions based on gaze @cite_29 @cite_8 . We note that while fingerprint based authentication is fast and convenient @cite_14 , it is only applicable to devices that invest in such equipment. In contrast, cameras are ubiquitously present, including on wearable devices such as smartwatches and smartglasses.
@cite_14 @cite_29 @cite_45 @cite_8
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The advent of commercial smartwatches provides an intriguing new platform for mobile biometrics. Like their smartphone counterparts, these mobile devices can perform gait-based biometric identification because they too contain an accelerometer and a gyroscope. However, smartwatches have several advantages over smartphones for biometric identification because users almost always wear their watch in the same location and orientation. This location (i.e. the wrist) tends to provide more information about a user's movements than the most common location for smartphones (pockets or handbags). In this paper we show the feasibility of using smartwatches for gait-based biometrics by demonstrating the high levels of accuracy that can result from smartwatch-based identification and authentication models. Applications of smartwatch-based biometrics range from a new authentication challenge for use in a multifactor authentication system to automatic personalization by identifying the user of a shared device. We examine three biometric authentication modalities -- voice, face and gesture -- as well as password entry, on a mobile device, to explore the relative demands on user time, effort, error and task disruption. Our laboratory study provided observations of user actions, strategies, and reactions to the authentication methods. Face and voice biometrics conditions were faster than password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term memory recall was better in the face verification condition. None of the authentication conditions were considered very usable. In conditions that combined two biometric entry methods, the time to acquire the biometric samples was shorter than if acquired separately but they were very unpopular and had high memory task error rates. These quantitative results demonstrate cognitive and motor differences between biometric authentication modalities, and inform policy decisions in selecting authentication methods. Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. This paper focuses on user authentication mechanisms for the Google Glass device (Glass). Glass only has three sources of input: a camera, a microphone, and a touchpad. This limited set of interfaces makes the use of standard passwords infeasible or cumbersome. We therefore propose a One-Time-Password (OTP) authentication scheme, Glass OTP that uses the Glass camera to scan a QR code displayed on the user’s smartphone. We implement a proof of concept Glass lock screen which unlocks only upon scanning an OTP generated by the companion Android smartphone application. We also discuss the reliability, security, and convenience of the proposed solution as compared to the current solutions in use. Because biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. It is important that such biometrics-based authentication systems be designed to withstand attacks when employed in security-critical applications, especially in unattended remote applications such as e-commerce. In this paper we outline the inherent strengths of biometrics-based authentication, identify the weak links in systems employing biometrics-based authentication, and present new solutions for eliminating some of these weak links. Although, for illustration purposes, fingerprint authentication is used throughout, our analysis extends to other biometrics-based methods. Credit card fraud is a common problem in today's world. Financial institutions have registered major loses till today due to users being exposed of their credit card information. Shoulder-surfing or observation attacks, including card skimming and video recording with hidden cameras while users perform PIN-based authentication at ATM terminals is one of the common threats for common users. Researchers have struggled to come up with secure solutions for secure PIN authentication. However, modern day ubiquitous wearable devices, such as the Google Glass have presented us with newer opportunities in this research area. In this paper, we propose Secure-PIN-Authentication-as-a-Service (SEPIA), a secure obfuscated PIN authentication protocol for ATM and other point-of-service terminals using cloud-connected personal mobile and wearable devices. Our approach protects the user from shoulder-surfers and partial observation attacks, and is also resistant to relay, replay, and intermediate transaction attacks. A SEPIA user utilizes a Google Glass or a mobile device for scanning a QR code on the terminal screen to prove co-location to the cloud-based server and obtain a secure PIN template for point-of-service authentication. SEPIA ensures minimal task overhead on the user's device with maximal computation offloaded to the cloud. We have implemented a proof-of-concept prototype to perform experimental analysis and a usability study for the SEPIA architecture. While biometrics have long been promoted as the future of authentication, the recent introduction of Android face unlock and iPhone fingerprint unlock are among the first large-scale deployments of biometrics for consumers. In a 10participant, within-subjects lab study and a 198-participant online survey, we investigated the usability of these schemes, along with users’ experiences, attitudes, and adoption decisions. Participants in our lab study found both face unlock and fingerprint unlock easy to use in typical scenarios. The notable exception was that face unlock was completely unusable in a dark room. Most participants preferred fingerprint unlock over face unlock or a PIN. In our survey, most fingerprint unlock users perceived it as more secure and convenient than a PIN. In contrast, face unlock users had mixed experiences, and many had stopped using it. We conclude with design recommendations for biometric authentication on smartphones. Face authentication is commonly offered as an alternative to passwords for device unlock. However, available face authentication systems are vulnerable to simple spoofing attacks. We demonstrate the impact of image quality on spoofing, using low resolution photo representative of those commonly posted online. We also show that videos and slideshows of images at different angles, and crude 3D avatars are effective. To defend against these vulnerabilities, we propose a face authentication system that includes a secrecy challenge. We present SAFE (Secure Authentication with Face and Eyes1), an improved face authentication method that uses a commodity gaze tracker to input a secret. During authentication, the user must not only show her face but also gaze at a secret icon that moves across the screen. Using a novel method for estimating the noise level in the gaze tracking data, SAFE adapts the system's parameters to enable secure, hands-free authentication. Wearable devices are computationally rich, portable and wearable to suit users in various needs and situations. Still, users of wearable devices suffer from lack of efficient input methods and proper feedback modality. In this paper, we design and implement a novel input method exploiting readily available ambient light sensor on wearable devices. We present concepts and implementation results of re-purposing the light sensor for user input and processing UI events. Furthermore, a PIN entry application based on the proposed input method is implemented to demonstrate a device authentication scenario for wearables. 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. This paper presents a novel unifying framework for electronic voting in the universal composability model that includes a property which is new to universal composability but well-known to voting systems: universal verifiability. Additionally, we propose three new techniques for secure electronic voting and prove their security and universal verifiability in the universal composability framework. 1. A tally-hiding voting system, in which the tally that is released consists of only the winner without the vote count. Our proposal builds on a novel solution to the millionaire problem which is of independent interest. 2. A self-tallying vote, in which the tally can be calculated by any observer as soon as the last vote has been cast — but before this happens, no information about the tally is leaked. 3. Authentication of voting credentials, which is a new approach for electronic voting systems based on anonymous credentials. In this approach, the vote authenticates the credential so that it cannot afterwards be used for any other purpose but to cast that vote. We propose a practical voting system that instantiates this high-level concept. Note. This is the extended version of the paper with the same title [33], which appeared in the USENIX Journal of Election Technology and Systems (JETS) [35]. Please cite that version instead. User authentication systems are at an impasse. The most ubiquitous method -- the password -- has numerous problems, including susceptibility to unintentional exposure via phishing and cross-site password reuse. Second-factor authentication schemes have the potential to increase security but face usability and deployability challenges. For example, conventional second-factor schemes change the user authentication experience. Furthermore, while more secure than passwords, second-factor schemes still fail to provide sufficient protection against (single-use) phishing attacks. We present PhoneAuth, a system intended to provide security assurances comparable to or greater than that of conventional two-factor authentication systems while offering the same authentication experience as traditional passwords alone. Our work leverages the following key insights. First, a user's personal device (eg a phone) can communicate directly with the user's computer (and hence the remote web server) without any interaction with the user. Second, it is possible to provide a layered approach to security, whereby a web server can enact different policies depending on whether or not the user's personal device is present. We describe and evaluate our server-side, Chromium web browser, and Android phone implementations of PhoneAuth. In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the user's unique head-movement patterns in response to an external audio stimulus. Compared to today's solutions, which primarily rely on indirect authentication mechanisms via the user's smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57 while keeping the average false acceptance rate of 4.43 . We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds. Implicit authentication schemes are a secondary authentication mechanism that provides authentication by employing unique patterns of device use that are gathered from smartphone users without requiring deliberate actions. Contemporary implicit authentication schemes operate at the device level such that they neither discriminate between data from different applications nor make any assumption about the nature of the application that the user is currently using. In this paper, we challenge the device-centric approach to implicit authentication on smartphones. We argue that the conventional approach of misuse detection at the device level has inherent limitations for mobile platforms. To this end, we analyze and empirically evaluate the device-centric nature of implicit authentication schemes to show their limitations in terms of detection accuracy, authentication overhead, and fine grained authentication control. To mitigate these limitations and for effective and pragmatic implicit authentication on the mobile platform, we propose a novel application-centric implicit authentication approach. We observe that for implicit authentication, an application knows best on when to authenticate and how to authenticate. Therefore, we delegate the implicit authentication task to the application and let the application provider decide when and how to authenticate a user in order to protect the owner's personal information. Our proposed application-centric implicit authentication approach improves accuracy and provides fine grained authentication control with low authentication overhead. Future research in this domain will benefit from our findings to provide pragmatic implicit authentication solutions. Abstract Biometrics authentication offers many advantages over conventional authentication systems that rely on possessions or special knowledge. With conventional technology, often the mere possession of an employee ID card is proof of ID, while a password potentially can be used by large groups of colleagues for long times without change. The fact that biometrics authentication is non-repudiable (hard to refute) and, yet, convenient, is among its most important advantages. Biometrics systems, however, suffer from some inherent biometrics-specific security threats. These threats are mainly related to the use of digital signals and the need for additional input devices, though we also discuss brute-force attacks of biometrics systems. There are also problems common to any pattern recognition system. These include “wolves” and “lambs”, and a new group we call “chameleons”. An additional issue with the use of biometrics is the invasion of privacy because the user has to enroll with an image of a body part. We discuss these issues and suggest some methods for mitigating their impact. Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent post-login authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66 and False Reject Rate of 0.13 for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users. Common authentication methods based on passwords, tokens, or fingerprints perform one-time authentication and rely on users to log out from the computer terminal when they leave. Users often do not log out, however, which is a security risk. The most common solution, inactivity timeouts, inevitably fail security (too long a timeout) or usability (too short a timeout) goals. One solution is to authenticate users continuously while they are using the terminal and automatically log them out when they leave. Several solutions are based on user proximity, but these are not sufficient: they only confirm whether the user is nearby but not whether the user is actually using the terminal. Proposed solutions based on behavioral biometric authentication (e.g., keystroke dynamics) may not be reliable, as a recent study suggests. To address this problem we propose Zero-Effort Bilateral Recurring Authentication (ZEBRA). In ZEBRA, a user wears a bracelet (with a built-in accelerometer, gyroscope, and radio) on her dominant wrist. When the user interacts with a computer terminal, the bracelet records the wrist movement, processes it, and sends it to the terminal. The terminal compares the wrist movement with the inputs it receives from the user (via keyboard and mouse), and confirms the continued presence of the user only if they correlate. Because the bracelet is on the same hand that provides inputs to the terminal, the accelerometer and gyroscope data and input events received by the terminal should correlate because their source is the same - the user's hand movement. In our experiments ZEBRA performed continuous authentication with 85 accuracy in verifying the correct user and identified all adversaries within 11s. For a different threshold that trades security for usability, ZEBRA correctly verified 90 of users and identified all adversaries within 50s. Secure communication over wireless channels necessitates authentication of communication partners to prevent man-in-the-middle attacks. For spontaneous interaction between independent, mobile devices, no a priori information is available for authentication purposes. However, traditional approaches based on manual password input or verification of key fingerprints do not scale to tens to hundreds of interactions a day, as envisioned by future ubiquitous computing environments. One possibility to solve this problem is authentication based on similar sensor data: when two (or multiple) devices are in the same situation, and thus experience the same sensor readings, this constitutes shared, (weakly) secret information. This paper introduces the Candidate Key Protocol (CKP) to interactively generate secret shared keys from similar sensor data streams. It is suitable for two-party and multi-party authentication, and supports opportunistic authentication. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. A new multi-modal biometric authentication approach using gait signals and fingerprint images as biometric traits is proposed. The individual comparison scores derived from the gait and fingers are normalized using four methods (min-max, z-score, median absolute deviation, tangent hyperbolic) and then four fusion approaches (simple sum, user-weighting, maximum score and minimum core) are applied. Gait samples are obtained by using a dedicated accelerometer sensor attached to the hip. The proposed method is evaluated using 7200 fingerprint images and gait samples. Fingerprints are collected by a capacitive line sensor, an optical sensor with total internal reflection and a touch-less optical sensor. The fusion results of these two biometrics show an improved performance and a large step closer for user authentication on mobile devices. PINs and patterns remain among the most widely used knowledge-based authentication schemes. As thermal cameras become ubiquitous and affordable, we foresee a new form of threat to user privacy on mobile devices. Thermal cameras allow performing thermal attacks, where heat traces, resulting from authentication, can be used to reconstruct passwords. In this work we investigate in details the viability of exploiting thermal imaging to infer PINs and patterns on mobile devices. We present a study (N=18) where we evaluated how properties of PINs and patterns influence their thermal attacks resistance. We found that thermal attacks are indeed viable on mobile devices; overlapping patterns significantly decrease successful thermal attack rate from 100 to 16.67 , while PINs remain vulnerable (>72 success rate) even with duplicate digits. We conclude by recommendations for users and designers of authentication schemes on how to resist thermal attacks.