Gruschka, Nils; Mavroeidis, Vasileios; Vishi, Kamer & Jensen, Meiko (2018). Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR, In Naoki Abe; Huan Liu; Xiaohua Hu; Nesreen Ahmed; Mu Qiao; Yang Song; Donald Kossmann; Bing Liu; Kisung Lee; Jiliang Tang; Jingrui He & Jeffrey Saltz (ed.),
2018 IEEE International Conference on Big Data (Big Data), Seattle, 10-13 Dec. 2018.
IEEE.
ISBN 978-1-5386-5035-6.
1.
s 5027
- 5033
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Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy of individuals and violate data protection laws. As a consequence, data controllers and data processors may be imposed tough penalties for non-compliance that can result even to bankruptcy. In this paper, we discuss the current state of the legal regulations and analyse different data protection and privacy-preserving techniques in the context of big data analysis. In addition, we present and analyse two real-life research projects as case studies dealing with sensitive data and actions for complying with the data regulation laws. We show which types of information might become a privacy risk, the employed privacy-preserving techniques in accordance with the legal requirements, and the influence of these techniques on the data processing phase and the research results.
This paper presents PS0, an ontological framework and a methodology for improving physical security and insider threat detection. PS0 can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. In all too many cases, rule-based anomaly detection can detect employee deviations from organizational security policies. In addition, PS0 can be considered a security provenance solution because of its ability to fully reconstruct attack patterns. Provenance graphs can be further analyzed to identify deceptive actions and overcome analytical mistakes that can result in bad decision-making, such as false attribution. Moreover, the information can be used to enrich the available intelligence (about intrusion attempts) that can form use cases to detect and remediate limitations in the system, such as loosely-coupled provenance graphs that in many cases indicate weaknesses in the physical security architecture. Ultimately, validation of the framework through use cases demonstrates and proves that PS0 can improve an organization's security posture in terms of physical security and insider threat detection.
The aim of this paper is to elucidate the implications of quantum computing in present cryptography and to introduce the reader to basic post-quantum algorithms. In particular the reader can delve into the following subjects: present cryptographic schemes (symmetric and asymmetric), differences between quantum and classical computing, challenges in quantum computing, quantum algorithms (Shor’s and Grover’s), public key encryption schemes affected, symmetric schemes affected, the impact on hash functions, and post quantum cryptography. Specifically, the section of Post-Quantum Cryptography deals with different quantum key distribution methods and mathematical-based solutions, such as the BB84 protocol, lattice-based cryptography, multivariate-based cryptography, hash-based signatures and code-based cryptography.
Vishi, Kamer & Yayilgan, Sule Yildirim (2013). Multimodal Biometric Authentication using Fingerprint and Iris Recognition in Identity Management, In Ke-Bin Jia; Jeng-Shyang Pan; Yao Zhao & Lakhmi C. Jain (ed.),
2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2013, Proceedings; 16-18 October 2013, Beijing, China.
IEEE.
ISBN 9780769551203.
Kapittel.
s 334
- 341
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The majority of deployed biometric systems today use information from a single biometric technology for verification or identification. Large-scale biometric systems have to address additional demands such as larger population coverage and demographic diversity, varied deployment environment, and more demanding performance requirements. Today’s single modality biometric systems are finding it difficult to meet these demands, and a solution is to integrate additional sources of information to strengthen the decision process. A multibiometric system combines information from multiple biometric traits, algorithms, sensors, and other components to make a recognition decision. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. The last 5 years have seen an exponential growth in research and commercialization activities in this area, and this trend is likely to continue. Therefore, here we propose a novel multimodal biometric authentication approach fusing iris and fingerprint traits at score-level. We principally explore the fusion of iris and fingerprint biometrics and their potential application as biometric identifiers. The individual comparison scores obtained from the iris and fingerprints are combined at score-level using a three score normalization techniques (Min-Max, Z-Score, Hyperbolic Tangent) and four score fusion approaches (Minimum Score, Maximum Score Simple Sum and User Weighting). The fused-score is utilized to classify an unknown user into the genuine or impostor.
Vishi, Kamer & Yildirim, Sule (2013). Multimodal Biometric Authentication Using Fingerprint and Iris Recognition in Identity Management.
IEEE-2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP).
ISBN 978-0-7695-5120-3.
8 s.
