52 research outputs found
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Lightweight Static and Dynamic Attributes Based Access Control Scheme for Secure Data Access in Mobile Environment
Technology advancements in smart mobile devices empower mobile users by enhancing mobility, customizability and adaptability of computing environments. Mobile devices are now intelligent enough to capture dynamic attributes such as unlock failures, application usage, location and proximity of devices in and around its surrounding environment. Different users will have different set of values for these dynamic attributes. In traditional attribute based access control, users are authenticated to access restricted data using long term static attributes such as password, roles, and physical location. In this paper, in order to allow secure data access in mobile environment, we securely combine both the dynamic and static attributes and develop novel access control technique. Security and performance analyse show that the proposed scheme substantially reduces the computational complexity while enhances the security compare to the conventional schemes
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Privacy-Preserving iVector-Based Speaker Verification
This paper introduces an efficient algorithm to develop a privacy-preserving voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice print instead of alphanumeric passwords. Since a voice print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this paper proposes a novel technique based on randomization to carry out voice authentication, which allows the user to enrol and verify their voice in the randomized domain. To achieve this, the iVector-based voice verification technique has been redesigned to work on the randomized domain. The proposed algorithm is validated using a well-known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomization operations
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A New Lightweight Symmetric Searchable Encryption Scheme for String Identification
In this paper, we provide an efficient and easy-to-implement symmetric searchable encryption scheme (SSE) for string search, which takes one round of communication, O(n) times of computations over n documents. Unlike previous schemes, we use hash-chaining instead of chain of encryption operations for index generation, which makes it suitable for lightweight applications. Unlike the previous SSE schemes for string search, with our scheme, server learns nothing about the frequency and the relative positions of the words being searched except what it can learn from the history. We are the first to propose probabilistic trapdoors in SSE for string search. We provide concrete proof of non-adaptive security of our scheme against honest-but-curious server based on the definitions of [12]. We also introduce a new notion of search pattern privacy, which gives a measure of security against the leakage from trapdoor. We have shown that our scheme is secure under search pattern indistinguishability definition. We show why SSE scheme for string search cannot attain adaptive indistinguishability criteria as mentioned in [12]. We also propose modifications of our scheme so that the scheme can be used against active adversaries at the cost of more rounds of communications and memory space. We validate our scheme against two different commercial datasets (see [1],[2])
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MMSE-based beamforming techniques for relay broadcast channels
We propose minimum mean square error (MMSE) based beamforming techniques for a multiantenna relay network, where a base station (BS) equipped with multiple antennas communicates with a number of single antenna users through a multiantenna relay.We specifically solve three optimization problems: a) sum-power minimization problem b) mean square error (MSE) balancing problem and c)mixed quality of services (QoS) problem. Unfortunately, these problems are not jointly convex in terms of beamforming vectors at the BS and the relay amplification matrix. To circumvent this non-convexity issue, the original problems are divided into two subproblems where the beamforming vectors and the relay amplification matrix are alternately optimized while other one is fixed. Three iterative algorithms have been developed based on convex optimization techniques and general MSE duality. Simulation results have been provided to validate the convergence of the proposed algorithms
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Smart, secure and seamless access control scheme for mobile devices
Smart devices capture users' activity such as unlock failures, application usage, location and proximity of devices in and around their surrounding environment. This activity information varies between users and can be used as digital fingerprints of the users' behaviour. Traditionally, users are authenticated to access restricted data using long term static attributes such as password and roles. In this paper, in order to allow secure and seamless data access in mobile environment, we combine both the user behaviour captured by the smart device and the static attributes to develop a novel access control technique. Security and performance analyses show that the proposed scheme substantially reduces the computational complexity while enhances the security compared to the conventional schemes
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A New Secure and Lightweight Searchable Encryption Scheme over Encrypted Cloud Data
Searchable Encryption is an emerging cryptographic technique that enables searching capabilities over the encrypted data on the cloud. In this paper, a novel searchable encryption scheme for the client-server architecture has been presented. The scheme exploits the properties of modular inverse to generate a probabilistic trapdoor which facilitates the searching over the secure inverted index table. We propose indistinguishability that is achieved by using the property of a probabilistic trapdoor. We design and implement a proof of concept prototype and test our scheme onto a real dataset of files. We analyze the performance of our scheme against our claim of the scheme being light weight. The security analysis yields that our scheme assures higher level of security as compared to other existing schemes
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E-mail address categorization based on semantics of surnames
Surname (family name) analysis is used in geography to understand population origins, migration, identity, social norms and cultural customs. Some of these are supposedly evolved over generations. Surnames exhibit good statistical properties that can be used to extract information in names data set such as automatic detection of ethnic or community groups in names. An e-mail address, often contains surname as a substring. This containment may be full or partial. An e-mail address categorization based on semantics of surnames is the objective of this paper. This is achieved in two phases. First phase deals with surname representation and clustering. Here, a vector space model is proposed where latent semantic analysis is performed. Clustering is done using the method called averagelinkage method. In the second phase, an email is categorized as belonging to one of the categories (discovered in first phase). For this, substring matching is required, which is done in an efficient way by using suffix tree data structure. We perform experimental evaluation for the 500 most frequently occurring surnames in India and United Kingdom. Also, we categorize the e-mail addresses that have these surnames as substrings
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PIndroid: A novel Android malware detection system using ensemble learning methods
The extensive use of smartphones has been a major driving force behind a drastic increase of malware attacks. Covert techniques used by the malware make them hard to detect with signature based methods. In this paper, we present PIndroid – a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with Ensemble methods for accurate malware detection. The proposed approach, when applied to 1,745 real world applications, provides 99.8% accuracy (which is best reported to date). Empirical results suggest that the proposed framework is effective in detection of malware apps
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An Analysis of Tracking Service Settings in Blackberry 10 and Windows Phone 8 Smartphones
The use of tracking settings in smartphones facilitates the provision of tailored services to users by allowing service providers access to unique identifiers stored on the smartphones. In this paper, we investigate the `tracking of' settings on the Blackberry 10 and Windows Phone 8 platforms. To determine if they work as claimed, we set up a test bed suitable for both operating systems to capture traffic between the smartphone and external servers. We dynamically execute a set of similar Blackberry 10 and Windows Phone 8 applications, downloaded from their respective official markets. Our results indicate that even if users turn of tracking settings in their smartphones, some applications leak unique identifiers without their knowledge
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Spontaneous expression classification in the encrypted domain
To date, most facial expression analysis have been based on posed image databases and is carried out without being able to protect the identity of the subjects whose expressions are being recognised. In this paper, we propose and implement a system for classifying facial expressions of images in the encrypted domain based on a Paillier cryptosystem implementation of Fisher Linear Discriminant Analysis and k-nearest neighbour (FLDA + kNN). We present results of experiments carried out on a recently developed natural visible and infrared facial expression (NVIE) database of spontaneous images. To the best of our knowledge, this is the first system that will allow the recog-nition of encrypted spontaneous facial expressions by a remote server on behalf of a client
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