Preserving Privacy in On-Line Analytical Processing (OLAP)

This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. It reviews a series of methods that can precisely answer data cube-style OLAP, regarding sensitive data while provably preventing adversaries from inferring data.

Addresses the privacy issue of On-Line Analytic Processing systems

Details how to keep the performance overhead of these security methods at a reasonable level

Examines how a balance between security, availability, and performance can feasibly be achieved in OLAP systems

First book that concentrates solely on OLAP systems Includes Lattice-Based Inference Control Method Discusses methods that can be implemented on the basis of \emph(three-Tier) Inference control model in OLAP systems Includes supplementary material: sn.pub/extras

Autorentext

Suryadipta Majumdar is currently an Assistant Professor in the Information Security and Digital Forensics department at University at Albany - SUNY. Suryadipta received his Ph.D. on cloud security auditing from Concordia University, Canada. His research mainly focuses on cloud security, Software Defined Network (SDN) security and Internet of Things (IoT) security. Taous Madi is currently an Experienced Researcher at Ericsson Canada. She holds a Ph.D. in Information Systems Engineering from Concordia University, Montreal. Previously, she finished her M.Sc. in mobile computing from the University of Science and Technology Houari Boumedien (USTHB), Algiers. Her research interests include cloud computing, network function virtualization security, software-defined networking security, internet of things security, machine learning and formal methods. Yushun Wang completed his MASc in Information System Security from Concordia University. Previously, he worked as a customer network support engineer, Ericsson (China) for 12 years. He is currently working as a developer at Above Security Canada. Azadeh Tabiban is currently a Ph.D. student at Concordia Institute for Information Systems Engineering (CIISE). She obtained her Master's degree in Information Systems Security from Concordia University, where she contributed to several research projects on cloud security auditing. Her research interest focuses on the development of more accountable and transparent virtualized environments; particularly on cloud and NFV provenance, intrusion detection, proactive auditing and secure virtualization in untrusted cloud infrastructures. Momen Oqaily is currently working toward the Ph.D. degree in information and systems engineering at the Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada. He received hisB.S. degree in network engineering and security and the master's degree in Information Systems Engineering. His research interests include privacy and cloud computing security auditing. Amir Alimohammadifar completed his BSc in Information Technology in 2010. He received his first master's in Information Technology, communication and computer networks from Sharif University of Technology, 2012, and his second master's in Information Systems Security from Concordia University. He is currently working as a security DevOps engineer at Gameloft Canada. Yosr Jarraya is currently a researcher in security at Ericsson. Before that, she had a two-year MITACS postdoctoral fellowship with the company. She was previously Research Associate and Postdoctoral Fellow at Concordia University, Montreal. She received a Ph.D. in Electrical and Computer Engineering from Concordia University. She is the co-author of more than 30 research paperson topics including cloud security, network and software security, formal verification and SDN. Makan Pourzandi is a research leader at Ericsson, Canada. He received his Ph.D. degree in Computer Science from the University of Lyon I Claude Bernard, France and a M.Sc. in parallel computing from École Normale Supérieure de Lyon, France. He has more than 20 years of experience in the fields of cyber security, Telecom and distributed systems. He co-authored a book on software security published by Springer on 2015 and is the co-inventor of 19 granted US patents. He has published more than 65 research papers in peer-reviewed scientific journals and conferences. Lingyu Wang is a Professor at the Concordia Institute for Information Systems Engineering (CIISE) at Concordia University, Montreal, Canada. He holds the NSERC/Ericsson Senior Industrial Research Chair in SDN/NFV Security. He received his Ph.D. degree in Information Technology in 2006 from George Mason University. His research interests include cloud computing sec

Klappentext

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.

Zusammenfassung

Addresses the privacy issue of On-Line Analytic Processing systems

Details how to keep the performance overhead of these security methods at a reasonable level

Examines how a balance between security, availability, and performance can feasibly be achieved in OLAP systems

Inhalt
OLAP and Data Cubes.- Inference Control in Statistical Databases.- Inferences in Data Cubes.- Cardinality-based Inference Control.- Parity-based Inference Control for Range Queries.- Lattice-based Inference Control in Data Cubes.- Query-driven Inference Control in Data Cubes.- Conclusion and Future Direction.