Bøger af Lingyu Wang

Digital forensics deals with the investigation of cybercrimes. With the growing deployment of cloud computing, mobile computing, and digital banking on the internet, the nature of digital forensics has evolved in recent years, and will continue to do so in the near future.This book presents state-of-the-art techniques to address imminent challenges in digital forensics. In particular, it focuses on cloud forensics, Internet-of-Things (IoT) forensics, and network forensics, elaborating on innovative techniques, including algorithms, implementation details and performance analysis, to demonstrate their practicality and efficacy. The innovations presented in this volume are designed to help various stakeholders with the state-of-the-art digital forensics techniques to understand the real world problems. Lastly, the book will answer the following questions: How do the innovations in digital forensics evolve with the emerging technologies? What are the newest challenges in the field of digital forensics?

On-Line Analytic Processing (OLAP) systems usually need to meet two conflicting goals. First, the sensitive data stored in underlying data warehouses must be kept secret. Second, analytical queries about the data must be allowed for decision support purposes. The main challenge is that sensitive data can be inferred from answers to seemingly innocent aggregations of the data. Existing inference control methods in statistical databases usually exhibit high performance overhead and limited effectiveness when applied to OLAP systems.Preserving Privacy in On-Line Analytical Processing reviews a series of methods that can precisely answer data cube-style OLAP queries regarding sensitive data while provably preventing adversaries from inferring the data. How to keep the performance overhead of these security methods at a reasonable level is also addressed. Achieving a balance between security, availability, and performance is shown to be feasible in OLAP systems.Preserving Privacy in On-Line Analytical Processing is designed for the professional market, composed of practitioners and researchers in industry.  This book is also appropriate for graduate-level students in computer science and engineering.

This book addresses automated software fingerprinting in binary code, especially for cybersecurity applications.

This book offers a novel approach to data privacy by unifying side-channel attacks within a general conceptual framework. Next, the book explores privacy-preserving traffic padding in Web applications, first via a model to quantify privacy and cost and then by introducing randomness to provide background knowledge-resistant privacy guarantee.

In addition, a weaving framework, with the underlying theoretical foundations, has been designed for the systematic injection of security aspects into UML models.The work is organized as follows: chapter 1 presents an introduction to software security, model-driven engineering, UML and aspect-oriented technologies.