Efficient privacy-preserving facial expression classification

This paper proposes an efficient algorithm to perform privacy-preserving (PP) facial expression classification (FEC) in the client-server model. The server holds a database and offers the classification service to the clients. The client uses the service to classify the facial expression (FaE) of subject. It should be noted that the client and server are mutually untrusted parties and they want to perform the classification without revealing their inputs to each other. In contrast to the existing works, which rely on computationally expensive cryptographic operations, this paper proposes a lightweight algorithm based on the randomization technique. The proposed algorithm is validated using the widely used JAFFE and MUG FaE databases. Experimental results demonstrate that the proposed algorithm does not degrade the performance compared to existing works. However, it preserves the privacy of inputs while improving the computational complexity by 120 times and communication complexity by 31 percent against the existing homomorphic cryptography based approach

Hide-and-seek: face recognition in private

Recent trend towards cloud computing and outsourcing has led to the requirement for face recognition (FR) to be performed remotely by third-party servers. When outsourcing the FR, client's test image and classification result will be revealed to the servers. Within this context, we propose a novel privacy-preserving (PP) FR algorithm based on randomization. Existing PP FR algorithms are based on homomorphic encryption (HE) which requires higher computational power and communication bandwidth. Since we use randomization, the proposed algorithm outperforms the HE based algorithm in terms of computational and communication complexity. We validated our algorithm using popular ORL database. Experimental results demonstrate that accuracy of the proposed algorithm is the same as the accuracy of existing algorithms, while improving the computational efficiency by 120 times and communication complexity by 2.5 times against the existing HE based approach

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

Robust access control framework for mobile cloud computing network

Unified communications has enabled seamless data sharing between multiple devices running on various platforms. Traditionally, organizations use local servers to store data and employees access the data using desktops with predefined security policies. In the era of unified communications, employees exploit the advantages of smart devices and 4G wireless technology to access the data from anywhere and anytime. Security protocols such as access control designed for traditional setup are not sufficient when integrating mobile devices with organization's internal network. Within this context, we exploit the features of smart devices to enhance the security of the traditional access control technique. Dynamic attributes in smart devices such as unlock failures, application usage, location and proximity of devices can be used to determine the risk level of an end-user. In this paper, we seamlessly incorporate the dynamic attributes to the conventional access control scheme. Inclusion of dynamic attributes provides an additional layer of security to the conventional access control. We demonstrate that the efficiency of the proposed algorithm is comparable to the efficiency of the conventional schemes

PIndroid: A novel Android malware detection system using ensemble learning

The extensive usage of smartphones has been the major driving force behind a drastic increase of new security threats. The stealthy techniques used by malware make them hard to detect with signature based intrusion detection and anti-malware 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 multiple stages of classifiers for malware detection. Ensemble techniques are applied for optimization of detection results. We apply the proposed approach on 1,745 real world applications and obtain 99.8% accuracy which is the best reported to date. Empirical results suggest that our proposed framework built on permissions and intents is effective in detecting malware applications

SmartARM: A smartphone-based group activity recognition and monitoring scheme for military applications

© 2017 IEEE. In this paper we propose SmartARM-A Smartphone-based group Activity Recognition and Monitoring (ARM) scheme, which is capable of recognizing and centrally monitoring coordinated group and individual group member activities of soldiers in the context of military excercises. In this implementation, we specifically consider military operations, where the group members perform similar motions or manoeuvres on a mission. Additionally, remote administrators at the command center receive data from the smartphones on a central server, enabling them to visualize and monitor the overall status of soldiers in situations such as battlefields, urban operations and during soldier's physical training. This work establishes-(a) the optimum position of smartphone placement on a soldier, (b) the optimum classifier to use from a given set of options, and (c) the minimum sensors or sensor combinations to use for reliable detection of physical activities, while reducing the data-load on the network. The activity recognition modules using the selected classifiers are trained on available data-sets using a test-train-validation split approach. The trained models are used for recognizing activities from live smartphone data. The proposed activity detection method puts forth an accuracy of 80% for real-time data

Privacy-preserving multi-class support vector machine for outsourcing the data classification in cloud

Emerging cloud computing infrastructure replaces traditional outsourcing techniques and provides flexible services to clients at different locations via Internet. This leads to the requirement for data classification to be performed by potentially untrusted servers in the cloud. Within this context, classifier built by the server can be utilized by clients in order to classify their own data samples over the cloud. In this paper, we study a privacy-preserving (PP) data classification technique where the server is unable to learn any knowledge about clients' input data samples while the server side classifier is also kept secret from the clients during the classification process. More specifically, to the best of our knowledge, we propose the first known client-server data classification protocol using support vector machine. The proposed protocol performs PP classification for both two-class and multi-class problems. The protocol exploits properties of Pailler homomorphic encryption and secure two-party computation. At the core of our protocol lies an efficient, novel protocol for securely obtaining the sign of Pailler encrypted numbers

Privacy-preserving clinical decision support system using gaussian kernel-based classification

A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised

Privacy-preserving iVector-based speaker verification

This work introduces an efficient algorithm to develop a privacy-preserving (PP) 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 work proposes a novel technique based on randomisation to carry out voice authentication, which allows the user to enrol and verify their voice in the randomised domain. To achieve this, the iVector based voice verification technique has been redesigned to work on the randomised 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 randomisation operations

User collusion avoidance scheme for privacy-preserving decentralized key-policy attribute-based encryption

Decentralized attribute-based encryption (ABE) is a variant of multi-authority based ABE whereby any attribute authority (AA) can independently join and leave the system without collaborating with the existing AAs. In this paper, we propose a user collusion avoidance scheme which preserves the user's privacy when they interact with multiple authorities to obtain decryption credentials. The proposed scheme mitigates the well-known user collusion security vulnerability found in previous schemes. We show that our scheme relies on the standard complexity assumption (decisional bilienar Deffie-Hellman assumption). This is contrast to previous schemes which relies on non-standard assumption (q-decisional Diffie-Hellman inversion)