Multi-biometric templates using fingerprint and voice

As biometrics gains popularity, there is an increasing concern about privacy and misuse of biometric data held in central repositories. Furthermore, biometric verification systems face challenges arising from noise and intra-class variations. To tackle both problems, a multimodal biometric verification system combining fingerprint and voice modalities is proposed. The system combines the two modalities at the template level, using multibiometric templates. The fusion of fingerprint and voice data successfully diminishes privacy concerns by hiding the minutiae points from the fingerprint, among the artificial points generated by the features obtained from the spoken utterance of the speaker. Equal error rates are observed to be under 2% for the system where 600 utterances from 30 people have been processed and fused with a database of 400 fingerprints from 200 individuals. Accuracy is increased compared to the previous results for voice verification over the same speaker database

Software for Wearable Devices: Challenges and Opportunities

Wearable devices are a new form of mobile computer system that provides exclusive and user-personalized services. Wearable devices bring new issues and challenges to computer science and technology. This paper summarizes the development process and the categories of wearable devices. In addition, we present new key issues arising in aspects of wearable devices, including operating systems, database management system, network communication protocol, application development platform, privacy and security, energy consumption, human-computer interaction, software engineering, and big data.Comment: 6 pages, 1 figure, for Compsac 201

Designing databases that enhance people's privacy without hindering organizations: Towards informational self-determination

We argue that future database systems must provide autonomy for individuals for the privacy of data they manage. We propose a design for such a system, identify challenges and problems, and suggest some approaches to these. We enunciate the reasons for informational self-determination systems, which include legal, organizational and technical issues. Our main goal is to achieve a widely-accepted realistic and practical solution in order to ensure privacy for individuals in our future world, yet without hindering business and securit

An improved Framework for Biometric Database’s privacy

Security and privacy are huge challenges in biometric systems. Biometrics are sensitive data that should be protected from any attacker and especially attackers targeting the confidentiality and integrity of biometric data. In this paper an extensive review of different physiological biometric techniques is provided. A comparative analysis of the various sus mentioned biometrics, including characteristics and properties is conducted. Qualitative and quantitative evaluation of the most relevant physiological biometrics is achieved. Furthermore, we propose a new framework for biometric database privacy. Our approach is based on the use of the promising fully homomorphic encryption technology. As a proof of concept, we establish an initial implementation of our security module using JAVA programming language

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

An Approach for Managing Access to Personal Information Using Ontology-Based Chains

The importance of electronic healthcare has caused numerous changes in both substantive and procedural aspects of healthcare processes. These changes have produced new challenges to patient privacy and information secrecy. Traditional privacy policies cannot respond to rapidly increased privacy needs of patients in electronic healthcare. Technically enforceable privacy policies are needed in order to protect patient privacy in modern healthcare with its cross organisational information sharing and decision making. This thesis proposes a personal information flow model that specifies a limited number of acts on this type of information. Ontology classified Chains of these acts can be used instead of the "intended/business purposes" used in privacy access control to seamlessly imbuing current healthcare applications and their supporting infrastructure with security and privacy functionality. In this thesis, we first introduce an integrated basic architecture, design principles, and implementation techniques for privacy-preserving data mining systems. We then discuss the key methods of privacypreserving data mining systems which include four main methods: Role based access control (RBAC), Hippocratic database, Chain method and eXtensible Access Control Markup Language (XACML). We found out that the traditional methods suffer from two main problems: complexity of privacy policy design and the lack of context flexibility that is needed while working in critical situations such as the one we find in hospitals. We present and compare strategies for realising these methods. Theoretical analysis and experimental evaluation show that our new method can generate accurate data mining models and safe data access management while protecting the privacy of the data being mined. The experiments followed comparative kind of experiments, to show the ease of the design first and then follow real scenarios to show the context flexibility in saving personal information privacy of our investigated method