Linked e-resources
Details
Table of Contents
Preface; Acknowledgments; Contents; 1 Introduction; 1.1 Background; 1.2 Overview; 1.3 Summary of Contributions; References; 2 Related Work; 2.1 Privacy Preservation; 2.2 Side-Channel Attacks; 2.3 Side-Channel Leaks in Data Publishing; 2.4 Side-Channel Leaks in Web Applications; 2.5 Side-Channel Leaks in Smart Metering; References; 3 Data Publishing: Trading Off Privacy with Utility Through the k-Jump Strategy; 3.1 Overview; 3.2 The Model; 3.2.1 The Algorithms anaive and asafe; 3.3 k-Jump Strategy; 3.3.1 The Algorithm Family ajump(k k k k); 3.3.2 Properties of ajump(k k k k)
3.4 Data Utility Comparison3.4.1 Data Utility of k-Jump Algorithms; 3.4.1.1 The Case of ajump(1) vs. ajump(i) (i>1); 3.4.1.2 The Case of ajump(i) vs. ajump(j) (1
4.1.1 Motivating Example4.2 The Model; 4.2.1 The Basic Model; 4.2.2 l-Candidate and Self-Contained Property; 4.2.3 Main Results; 4.3 The Algorithms; 4.3.1 The RIA Algorithm (Random and Independent); 4.3.2 The RDA Algorithm (Random and Dependent); 4.3.3 The GDA Algorithm (Guided and Dependent); 4.3.4 The Construction of SGSS; 4.4 Experiments; 4.4.1 Computation Overhead; 4.4.2 Data Utility; 4.5 Discussion; 4.6 Summary; References; 5 Web Applications: k-Indistinguishable Traffic Padding; 5.1 Overview; 5.2 The Model; 5.2.1 Basic Model; 5.2.2 Privacy and Cost Model; 5.3 PPTP Problem Formulation
5.3.1 SVSD and SVMD5.3.2 MVMD; 5.4 The Algorithms; 5.5 Extension to l-Diversity; 5.5.1 The Model and Problem Formulation; 5.5.2 The Algorithms; 5.6 Evaluation; 5.6.1 Implementation and Experimental Settings; 5.6.2 Communication Overhead; 5.6.3 Computational Overhead; 5.6.4 Processing Overhead; 5.7 Summary; References; 6 Web Applications: Background-Knowledge Resistant Random Padding; 6.1 Overview; 6.1.1 Motivating Example; 6.2 The Model; 6.2.1 Traffic Padding; 6.2.2 Privacy Properties; 6.2.3 Padding Method; 6.2.4 Cost Metrics; 6.3 The Algorithms; 6.3.1 The Random Ceiling Padding Scheme
6.3.2 Instantiations of the Scheme6.4 The Analysis; 6.4.1 Analysis of Privacy Preservation; 6.4.2 Analysis of Costs; 6.4.3 Analysis of Computational Complexity; 6.5 Experiment; 6.5.1 Experimental Setting; 6.5.2 Uncertainty and Cost vs k; 6.5.3 Randomness Drawn from Bounded Uniform Distribution; 6.5.4 Randomness Drawn from Normal Distribution; 6.6 Summary; References; 7 Smart Metering: Inferences of Appliance Status from Fine-Grained Readings; 7.1 Overview; 7.2 Motivating Example; 7.3 The Model; 7.3.1 Adversary Model; 7.3.2 Privacy Property; 7.3.3 Cost Metrics; 7.4 Summary; References
3.4 Data Utility Comparison3.4.1 Data Utility of k-Jump Algorithms; 3.4.1.1 The Case of ajump(1) vs. ajump(i) (i>1); 3.4.1.2 The Case of ajump(i) vs. ajump(j) (1
4.1.1 Motivating Example4.2 The Model; 4.2.1 The Basic Model; 4.2.2 l-Candidate and Self-Contained Property; 4.2.3 Main Results; 4.3 The Algorithms; 4.3.1 The RIA Algorithm (Random and Independent); 4.3.2 The RDA Algorithm (Random and Dependent); 4.3.3 The GDA Algorithm (Guided and Dependent); 4.3.4 The Construction of SGSS; 4.4 Experiments; 4.4.1 Computation Overhead; 4.4.2 Data Utility; 4.5 Discussion; 4.6 Summary; References; 5 Web Applications: k-Indistinguishable Traffic Padding; 5.1 Overview; 5.2 The Model; 5.2.1 Basic Model; 5.2.2 Privacy and Cost Model; 5.3 PPTP Problem Formulation
5.3.1 SVSD and SVMD5.3.2 MVMD; 5.4 The Algorithms; 5.5 Extension to l-Diversity; 5.5.1 The Model and Problem Formulation; 5.5.2 The Algorithms; 5.6 Evaluation; 5.6.1 Implementation and Experimental Settings; 5.6.2 Communication Overhead; 5.6.3 Computational Overhead; 5.6.4 Processing Overhead; 5.7 Summary; References; 6 Web Applications: Background-Knowledge Resistant Random Padding; 6.1 Overview; 6.1.1 Motivating Example; 6.2 The Model; 6.2.1 Traffic Padding; 6.2.2 Privacy Properties; 6.2.3 Padding Method; 6.2.4 Cost Metrics; 6.3 The Algorithms; 6.3.1 The Random Ceiling Padding Scheme
6.3.2 Instantiations of the Scheme6.4 The Analysis; 6.4.1 Analysis of Privacy Preservation; 6.4.2 Analysis of Costs; 6.4.3 Analysis of Computational Complexity; 6.5 Experiment; 6.5.1 Experimental Setting; 6.5.2 Uncertainty and Cost vs k; 6.5.3 Randomness Drawn from Bounded Uniform Distribution; 6.5.4 Randomness Drawn from Normal Distribution; 6.6 Summary; References; 7 Smart Metering: Inferences of Appliance Status from Fine-Grained Readings; 7.1 Overview; 7.2 Motivating Example; 7.3 The Model; 7.3.1 Adversary Model; 7.3.2 Privacy Property; 7.3.3 Cost Metrics; 7.4 Summary; References