Common smartphone authentication mechanisms (e.g., PINs, graphical passwords, and fingerprint scans) are not designed to offer security post-login. Multi-modal continuous authentication addresses this issue by frequently and unobtrusively authenticating the user via behavioral biometric signals, such as touchscreen interaction and hand movements. Because smartphones can easily fall into the hands of the adversary, it is critical that the behavioral biometric information collected and processed on these devices is secured. This can be done by offloading encrypted template information to a remote server, and then performing authentication via privacy-preserving protocols. In this paper, we demonstrate that the energy overhead of current privacy-preserving protocols for continuous authentication is unsustainable on smartphones. To reduce energy consumption, we design a technique that leverages characteristics unique to the authentication setting in order to securely outsource computation to an untrusted Cloud. Our approach is secure against a colluding smartphone and Cloud, thus making it well suited for authentication. We performed extensive experimental evaluation. With our technique, the energy requirement for running an authentication instance that computes Manhattan distance is 0.2 mWh, which corresponds to a negligible fraction of the smartphone's battery capacity. In addition, for Manhattan distance, our protocol runs in 0.72 and 2 s for 8 and 28 biometric features, respectively. We were also able to compute Hamming distance in 3.29 s, compared with 95.57 s achieved with the previous fastest outsourced computation protocol (Whitewash). These results demonstrate that ours is presently the only technique suitable for low-latency continuous authentication (e.g., with authentication scan windows of 60 s or shorter).

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

We design privacy-preserving protocols for scaled Manhattan and scaled Euclidean verifiers, secure against malicious clients and honest-but-curious server. We then augment our protocols with principal component analysis (PCA), which can help to improve authentication accuracy. We evaluate the performance of our protocols on an emerging application-namely, continuous authentication of smartphone users. We compare the performance of protocols secure under the malicious client model, with three protocols secure in the honest-but-curious model. We report tradeoffs between computation overhead, communication cost, and authentication accuracy. Our key observations are: 1) scaled Manhattan without PCA gives the best tradeoff between security, accuracy, and overhead and 2) with PCA, memory availability on current smartphones limits the number of features that can be used with scaled Manhattan, and prevents the scaled Euclidean protocol from running. Our extended evaluation on a laptop client shows that PCA with both scaled Manhattan and scaled Euclidean verifiers is feasible given sufficient memory.

In recent years, the availability of GPS-enabled smartphones have made location-based services extremely popular. A multitude of applications rely on location information to provide a wide range of services. Location information is, however, extremely sensitive and can be easily abused. In this paper, we introduce the first protocols for secure computation of distance and for proximity testing over a sphere. Our secure distance protocols allow two parties, Alice and Bob, to determine their mutual distance without disclosing any additional information about their location. Through our secure proximity testing protocols, Alice only learns if Bob is in close proximity, i.e., within some arbitrary distance. An important difference between our protocols and existing techniques is that our protocols are the first not to require parties to privately negotiate a common map. Our protocols rely on three different representations of Earth, which provide different trade-offs between accuracy and performance. We show, via experiments on a prototype implementation, that our protocols are practical on resource-constrained smartphone devices. Our distance computation protocols runs in 54 to 78 ms on a commodity Android smartphone. Similarly, our proximity tests require between 1.2 s and 2.8 s on the same platform. The imprecision introduced by our protocols is very small, i.e., between 0.1% and 2% on average, depending on the distance.

Biometric key generation techniques are used to reliably generate cryptographic material from biometric signals. Existing constructions require users to perform a particular activity (e.g., type or say a password, or provide a handwritten signature), and are therefore not suitable for generating keys continuously. In this paper we present a new technique for biometric key generation from free-text keystroke dynamics. This is the first technique suitable for continuous key generation. Our approach is based on a scaled parity code for key generation (and subsequent key reconstruction), and can be augmented with the use of population data to improve security and reduce key reconstruction error. In particular, we rely on linear discriminant analysis (LDA) to obtain a better representation of discriminable biometric signals. To update the LDA matrix without disclosing user's biometric information, we design a provably secure privacy-preserving protocol (PP-LDA) based on homomorphic encryption. Our biometric key generation with PP-LDA was evaluated on a dataset of 486 users. We report equal error rate around 5% when using LDA, and below 7% without LDA.