Security Infrastructure Technology for Integrated Utilization of Big Data : : Applied to the Living Safety and Medical Fields.

• Author: Miyaji, Atsuko.
• TeilnehmendeR: Mimoto, Tomoaki.
• Place / Publishing House: Singapore : : Springer Singapore Pte. Limited,, 2020.; Ã2020.
• Year of Publication: 2020
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (171 pages)

Table of Contents

• Intro
• Foreword
• Preface
• Contents
• 1 Introduction
• 1.1 Purpose of Miyaji-CREST
• 1.2 Roles of Each Group
• 1.2.1 Security Core Group
• 1.2.2 Security Management Group
• 1.2.3 Living Safety Testbed Group
• 1.2.4 Health Testbed Group
• 2 Cryptography Core Technology
• 2.1 Analysis on ECDLP
• 2.1.1 Introduction
• 2.1.2 Previous Works
• 2.1.3 Montgomery and Hessian Curves
• 2.1.4 Experiments on PDP Solving
• 2.1.5 Analysis
• 2.1.6 Concluding Remarks
• 2.2 Analysis on Ring-LWE over Decomposition Fields
• 2.2.1 Introduction
• 2.2.2 Preliminaries
• 2.2.3 Ring-LWE over Cyclotomic and Decomposition Fields
• 2.2.4 Our Experimental Analysis
• References
• 3 Secure Primitive for Big Data Utilization
• 3.1 Privacy-Preserving Data Integration
• 3.1.1 Introduction
• 3.1.2 Preliminaries
• 3.1.3 Previous Work
• 3.1.4 Practical MPSI
• 3.1.5 Efficiency
• 3.1.6 System and Performance
• 3.2 Classification
• 3.2.1 Error-Correcting Code
• 3.2.2 Security Assumptions
• 3.2.3 Security Requirements for 2PC
• 3.2.4 HQC Encryption Scheme
• 3.2.5 Proposed Protocol
• 3.2.6 Support Vector Machine from Secure Linear Function Evaluation and Secure Comparison
• References
• 4 Secure Data Management Technology
• 4.1 Introduction
• 4.2 Anonymization Techniques and Adversary Models, Revisited
• 4.2.1 k-Anonymization
• 4.2.2 Noise Addition
• 4.2.3 K-Anonymization for Combined Datasets
• 4.2.4 Matrix Factorization for Time-Sequence Data
• 4.2.5 Anonymization Techniques for User History Graphs
• 4.2.6 Other Notions
• 4.2.7 Combination of Anonymization Techniques
• 4.3 (p, N)-Identifiability
• 4.3.1 Common Adversary Model
• 4.3.2 Success Probability Analysis Based on the Common Adversary Model
• 4.3.3 Experiment
• 4.3.4 Results
• 4.4 Extension to Time-Sequence Data
• 4.4.1 Privacy Definition
• 4.4.2 Utility Definition.
• 4.4.3 Matrix Factorization
• 4.4.4 Anonymization Using Matrix Factorization
• 4.4.5 Experiment
• 4.4.6 Results
• 4.5 Anonymization and Privacy Risk Evaluation Tool
• 4.6 Conclusion
• References
• 5 Living Safety Testbed Group
• 5.1 Necessity of Living Safety
• 5.2 Overview of Test Bed System for Living Safety
• 5.3 Severity Cliff Analysis of School Injury
• 5.3.1 Development of Severity Cliff Analysis System
• 5.3.2 Severity Cliff Analysis
• 5.4 Trend Analysis of School Injury
• 5.4.1 Trend Analysis for Evaluating Intervention
• 5.4.2 Analysis of Judo Accident
• 5.5 Childhood Home-Injury Simulation
• 5.5.1 Background of Simulation
• 5.5.2 Home-Injury-Situation Simulation System
• 5.5.3 Development Behavior Prediction Function
• 5.5.4 Accident Situation Search Function
• 5.5.5 Similar-Risk-Product Classification Function
• 5.5.6 Simulation Example of the Accident Situation
• 5.5.7 System Verification
• 5.6 Social Impact Engagement Based on Big Data Analysis in Cooperation with Multiple Stakeholders
• 5.6.1 Engagement for Preventing Soccer Goal Turnover
• 5.6.2 Engagement for Preventing Vaulting Box Accidents
• 5.7 Conclusion
• References
• 6 Health Test Bed Group
• 6.1 Overview of Legislation and Standardization for the Secondary Use of Electronic Medical Records
• 6.1.1 Personal Information Protection Act and Next-Generation Medical Infrastructure Act
• 6.1.2 Ethical Guidelines and Anonymization of Medical Information
• 6.1.3 Standardization of EMRs
• 6.2 Medical Test Bed Concepts and Requirements
• 6.3 Features and Implementations of Secondary Use Infrastructure Development
• 6.3.1 SS-MIX2 Standardized Storage
• 6.3.2 Secure Collection of Distributed Medical Information
• 6.3.3 Privacy Risk Assessment of Extracted Datasets
• 6.3.4 Secondary Use and Traceability
• 6.4 Integration and Prospects
• References.