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E-grāmata: Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques

Edited by (Yahoo! Research, Barcelona, Spain), Edited by (University of Insubria, Italy)
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"Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results; they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. The book focuses on intricate, real-world applications in medicine, biology, the web, social networks, and mobility observation systems"--

"Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results. they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques thatcan be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility dataanalysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives.While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation"--

Provided by publisher.
Preface xv
About the Editors xxiii
List of Contributors
xxv
I General Techniques
1(108)
1 Anonymity Technologies for Privacy-Preserving Data Publishing and Mining
3(32)
Anna Monreale
Dino Pedreschi
Ruggero G. Pensa
1.1 Introduction
3(5)
1.1.1 Privacy vs. Utility
5(1)
1.1.2 Attacks and Countermeasures
5(1)
1.1.3 Privacy-Preserving Data Mining and Statistical Disclosure Control
6(1)
1.1.4 Anonymity in Data Protection Laws
6(1)
1.1.5 Anonymity in Data Protection Laws
6(2)
1.1.6 Plan of the
Chapter
8(1)
1.2 Anonymity for Data Publishing and Mining
8(5)
1.2.1 Anonymity by Randomization
8(3)
1.2.2 Anonymity by Indistinguishability
11(2)
1.3 Statistical Disclosure Control
13(8)
1.3.1 Non-Perturbative Masking Techniques
14(1)
1.3.2 Perturbative Masking Techniques
15(3)
1.3.3 Fully Synthetic Techniques
18(2)
1.3.4 Partially Synthetic Techniques
20(1)
1.4 Anonymity in Privacy Regulations
21(3)
1.4.1 Privacy Laws in Canada
21(1)
1.4.2 Privacy Laws in the United States
22(1)
1.4.3 Privacy Laws in the European Union
23(1)
1.5 Anonymity in Complex Data
24(3)
1.6 Conclusion
27(1)
References
28(7)
2 Privacy Preservation in the Publication of Sparse Multidimensional Data
35(24)
Manolis Terrovitis
Nikos Mamoulis
Panos Kalnis
2.1 Introduction
35(3)
2.2 Generic Methods
38(16)
2.2.1 Multirelational k-Anonymity
40(4)
2.2.2 l-Diversity for Sparse Multidimensional Data
44(4)
2.2.3 Km Anonymity
48(5)
2.2.4 Other Methods
53(1)
2.3 Privacy Preservation in Web Logs
54(1)
2.4 Conclusion
55(1)
References
56(3)
3 Knowledge Hiding in Emerging Application Domains
59(30)
Osman Abul
3.1 Introduction
59(2)
3.2 Knowledge Hiding
61(3)
3.3 Itemset Hiding
64(6)
3.3.1 Frequent Itemset Hiding
66(4)
3.4 Association Rule Hiding
70(2)
3.4.1 Association Rule Hiding
70(2)
3.4.2 Variations of Association Rule Mining
72(1)
3.5 Sequential Pattern Hiding
72(3)
3.5.1 Simple Sequences
73(1)
3.5.2 Saptio-Temporal Sequences
74(1)
3.6 Classification Rule Hiding
75(2)
3.7 Other Knowledge-Hiding Domains
77(4)
3.7.1 Cluster Hiding
77(1)
3.7.2 Outlier Hiding
78(1)
3.7.3 Text Hiding
78(1)
3.7.4 Link Hiding
79(1)
3.7.5 XML Hiding
80(1)
3.8 Meta-Knowledge Hiding
81(1)
3.9 Conclusion
82(1)
References
83(6)
4 Condensation-Based Methods in Emerging Application Domains
89(20)
Yucel Saygin
Mehmet Ercan Nergiz
4.1 Introduction
89(3)
4.2 Sequence Data Types
92(1)
4.3 An Overview of Mining and Privacy-Preserving Publishing of Sequence Data
93(1)
4.4 Condensation-Based Methods
94(9)
4.4.1 Privacy Metrics and Attack Models
95(1)
4.4.2 Comparison of Condensation-Based and Other Privacy Approaches
96(1)
4.4.3 Condensation-Based Methods for Tabular Data
97(2)
4.4.4 Condensation-Based Methods for Strings
99(1)
4.4.5 Condensation-Based Methods for Sequential Patterns
100(1)
4.4.6 Condensation-Based Methods for Trajectory Data
100(2)
4.4.7 Comparison of the Methods
102(1)
4.5 Conclusions and Open Research Problems
103(1)
References
104(5)
II Traces and Streams
109(52)
5 Catch, Clean, and Release: A Survey of Obstacles and Opportunities for Network Trace Sanitization
111(32)
Keren Tan
Jihwang Yeo
Michael E. Locasto
David Kotz
5.1 Introduction
111(7)
5.1.1 Challenges for Trace Collection and Sharing
112(1)
5.1.2 Real-World Network Trace Sharing Efforts
113(1)
5.1.3 Terminology
114(1)
5.1.4 Database Sanitization and Privacy-Preserving Data Mining
115(1)
5.1.5
Chapter Organization
116(2)
5.2 Sanitization
118(5)
5.2.1 Sanitization Techniques
118(3)
5.2.2 Sanitization Tools
121(2)
5.3 De-Sanitization
123(2)
5.4 Evaluation of Sanitization
125(3)
5.5 Challenges and Open Problems
128(2)
5.5.1 Quantifying Sensitive Information
128(1)
5.5.2 Metrics for Evaluating Sanitization Results
129(1)
5.5.3 Interpreting Sanitization Results
130(1)
5.6 Case Study: Dartmouth Internet Security Testbed
130(3)
5.7 Summary and Conclusion
133(2)
References
135(8)
6 Output Privacy in Stream Mining
143(18)
Ting Wang
Ling Liu
6.1 Introduction
143(2)
6.2 Grand Map of Privacy-Preserving Data Mining
145(3)
6.2.1 Input Privacy
145(1)
6.2.2 Output Privacy
146(2)
6.3 From Statistical Databases to Data Mining
148(3)
6.3.1 Reactive Approaches
149(1)
6.3.2 Proactive Approaches
149(2)
6.4 Case Study: Frequent-Pattern Mining over Streams
151(6)
6.4.1 Concepts and Models
151(1)
6.4.2 Breaches and Attacks
152(2)
6.4.3 Butterfly: A Proactive Solution
154(2)
6.4.4 Lessons Learned
156(1)
6.5 Roadmap for Future Research
157(1)
6.6 Conclusion
157(1)
References
158(3)
III Saptio-Temporal land Mobility Data
161(78)
7 Privacy Issues in Spatio-Temporal Data Mining
163(20)
Aris Gkoulalas-Divanis
Vassilios S. Verykios
7.1 Introduction
163(2)
7.2 Research Challenges
165(1)
7.3 A Taxonomy of the Privacy Methodologies
166(12)
7.3.1 Data Perturbation and Obfuscation
167(6)
7.3.2 Privacy-Aware Knowledge Sharing
173(2)
7.3.3 Sequential Pattern Hiding
175(3)
7.4 Roadmap and Future Trends
178(1)
7.5 Summary and Conclusions
179(1)
References
180(3)
8 Probabilistic Grid-Based Approaches for Privacy-Preserving Data Mining on Moving Object Trajectories
183(28)
Gyozo Gidofalvi
Xuegang Huang
Torben Bach Pedersen
8.1 Introduction
183(2)
8.2 Related Work
185(4)
8.2.1 General Privacy Concepts
185(1)
8.2.2 Location Privacy
186(1)
8.2.3 Spatio-Temporal Data Mining
187(2)
8.3 Spatio-Temporal Anonymization
189(4)
8.3.1 Definition of Location Privacy
189(2)
8.3.2 Practical "Cut-Enclose" Implementation
191(1)
8.3.3 Problems with Existing Methods
192(1)
8.4 Grid-Based Anonymization Framework
193(9)
8.4.1 Grid-Based Anonymization
193(2)
8.4.2 System Architecture
195(2)
8.4.3 Finding Dense Spatio-Temporal Areas
197(1)
8.4.4 Frequent Route Mining
198(2)
8.4.5 Multi-Grid Extension
200(2)
8.5 Evaluation
202(5)
8.5.1 Experimental Setup and Evaluation Criteria
202(1)
8.5.2 Dense ST-Area Queries
203(1)
8.5.3 Frequent Route Queries
204(3)
8.6 Conclusions and Future Work
207(1)
References
207(4)
9 Privacy and Anonymity in Location Data Management
211(28)
Claudio Bettini
Sergio Mascetti
Dario Freni
X. Sean Wang
Sushil Jajodia
9.1 Illustration of the Problem
211(4)
9.1.1 Typical Application Scenarios and Intuitive Threats
213(1)
9.1.2 The Need for Specialized Defense Techniques
214(1)
9.2 State of the Art
215(10)
9.2.1 Assumptions
215(3)
9.2.2 Techniques
218(2)
9.2.3 Literature Summary
220(5)
9.3 Emerging Techniques for Online Privacy Preservation in Location Data Management
225(7)
9.3.1 Historical Anonymization
225(3)
9.3.2 Location Obfuscation in Friend Finder Services
228(4)
9.4 Conclusions and Open Issues
232(2)
References
234(5)
IV Time Series
239(42)
10 Privacy Preservation on Time Series
241(24)
Spiros Papadimitriou
Feifei Li
George Kollios
Philip S. Yu
10.1 Introduction
241(2)
10.2 Background
243(3)
10.2.1 Discrete Wavelet Decomposition
244(1)
10.2.2 Compressibility and Shrinkage
245(1)
10.3 Privacy and Compression
246(5)
10.3.1 Intuition and Motivation
246(2)
10.3.2 Measuring Privacy
248(3)
10.4 Compressible Perturbation
251(7)
10.4.1 General Algorithm
251(1)
10.4.2 Batch Perturbation
252(4)
10.4.3 Streaming Perturbation
256(2)
10.5 Related Work
258(2)
10.6 Conclusion
260(1)
References
261(4)
11 A Segment-Based Approach to Preserve Privacy in Time Series Data Mining
265(16)
Yongjian Fu
Ye Zhu
11.1 Introduction
265(1)
11.2 Preserving Privacy in Time Series Data Mining
266(4)
11.2.1 Properties of Time Series
267(1)
11.2.2 Attacks on Time Series Privacy
267(1)
11.2.3 Methods fro Preserving Privacy
268(2)
11.3 A Segment-Based Approach for Preserving Privacy
270(1)
11.4 Experimental Results and Performance Evaluation
271(6)
11.4.1 Privacy Attacks and Classification Algorithm
271(1)
11.4.2 Performance Metrics
272(1)
11.4.3 Experimental Results in Privacy Preservation
273(2)
11.4.4 Experimental Results on Classification
275(2)
11.5 Conclusion
277(1)
References
277(4)
V Biomedical Data
281(70)
12 A Survey of Challenges and Solutions for Privacy in Clinical Genomics Data Mining
283(32)
Bradley Malin
Christopher Cassa
Murat Kantarcioglu
12.1 Introduction
283(2)
12.2 Data Sharing, Policy, and Potential Misuse
285(3)
12.2.1 Policies and Protections
286(2)
12.2.2 Implications for Family Members
288(1)
12.3 Technical Protections and Weaknesses
288(8)
12.3.1 A Model of Re-Identification
289(1)
12.3.2 From Genotype to Phenotype and Back Again
290(2)
12.3.3 We Are Family
292(1)
12.3.4 Following the Bread Crumbs
292(2)
12.3.5 Vulnerability Is a Matter of Context
294(1)
12.3.6 I Am My Sister's Keeper: Familial Inference of SNP Genotypes
294(2)
12.4 Achieving Formal Protection
296(5)
12.4.1 Protecting Privacy by Thwarting Linkage
297(2)
12.4.2 Protecting Privacy by Preventing Uniqueness
299(2)
12.5 Secure Multiparty Computation in the Biomedical Realm
301(1)
12.6 Discussion and the Future
301(3)
12.7 Conclusions
304(1)
References
305(10)
13 Privacy-Aware Health Information Sharing
315(36)
Thomas Trojer
Cheuk-Kwong Lee
Benjamin C. M. Fung
Lalita Narupiyakul
Patrick C. K. Hung
13.1 Introduction
315(2)
13.2 Reference Scenario
317(2)
13.3 Literature Review
319(9)
13.3.1 Record Linkage
320(5)
13.3.2 Attribute Linkage
325(3)
13.4 Privacy-Aware Information Sharing for Classification Analysis
328(13)
13.4.1 The Problem: Multi-QID k-Anonymity for Classification Analysis
328(2)
13.4.2 Masking Operations
330(1)
13.4.3 The Algorithm: Top-Down Refinement (TDR)
331(2)
13.4.4 The HL7-Compliant Data Structure for the Blood Usage Record
333(1)
13.4.5 Experimental Results
334(6)
13.4.6 Comparing with Other Algorithms
340(1)
13.5 Privacy-Aware Information Sharing for Cluster Analysis
341(4)
13.5.1 The Problem: Multi-QID k-Anonymity for Cluster Analysis
342(1)
13.5.2 The Solution Framework
342(2)
13.5.3 Evaluation
344(1)
13.6 Conclusions and Extensions
345(1)
References
346(5)
VI Web Usage Data
351(42)
14 Issues with Privacy Preservation in Query Log Mining
353(16)
Ricardo Baeza-Yates
Rosie Jones
Barbara Poblete
Myra Spiliopoulou
14.1 Introduction
353(1)
14.2 Private Information in Query Logs: Setting the Scene
354(5)
14.2.1 Which Information Is Private?
355(2)
14.2.2 Characteristics of a Query Log
357(1)
14.2.3 Sharing a Query Log
358(1)
14.2.4 The Risk of External Sources
358(1)
14.3 k-Anonymity in Query Log Privacy Protection
359(3)
14.4 Anonymization Techniques for Query Logs
362(3)
14.4.1 Measurement
362(1)
14.4.2 Aggregation and Generalization
363(1)
14.4.3 Suppression
364(1)
14.4.4 Adding Noise to Query Log Data
364(1)
14.5 Conclusions and Future Work
365(1)
References
365(4)
15 Preserving Privacy in Web Recommender Systems
369(24)
Ranieri Baraglia
Claudio Lucchese
Salvatore Orlando
Raffaele Perego
Fabrizio Silvestri
15.1 Introduction
369(2)
15.2 Taxonomy of Web Personalization and Recommendation
371(10)
15.2.1 Content-Based Filtering
373(1)
15.2.2 Collaborative Filtering
374(2)
15.2.3 Item-Based Collaborative Filtering
376(1)
15.2.4 Recommending by Clustering Unordered User Sessions
376(2)
15.2.5 Recommending through Association Analysis of Unordered User Sessions/Profiles
378(1)
15.2.6 Recommending by Clustering Ordered User Sessions
378(2)
15.2.7 Recommending through Sequential Analysis of Ordered User Sessions/Profiles
380(1)
15.3 The πSUGGEST System
381(2)
15.3.1 Privacy-Preserving Features of πSUGGEST
382(1)
15.4 πSUGGEST and Privacy
383(4)
15.4.1 Discussion
386(1)
15.5 πSUGGEST Evaluation
387(1)
15.6 Conclusion
388(1)
References
389(4)
VII Social Networks
393(106)
16 The Social Web and Privacy: Practices, Reciprocity and Conflict Detection in Social Networks
395(38)
Seda Gurses
Bettina Berendt
16.1 Introduction
395(3)
16.2 Approaching Privacy in Social Networks
398(8)
16.2.1 Data I: Personal Data
398(1)
16.2.2 Privacy as Hiding: Confidentiality
398(2)
16.2.3 Privacy as Control: Informational Self-Determination
400(1)
16.2.4 Privacy as Practice: Identity Construction
401(2)
16.2.5 Privacy in Social Network Sites: Deriving Requirements from Privacy Concerns
403(1)
16.2.6 Data II: Relational Information and Transitive Access Control
404(2)
16.3 Relational Information, Transitive Access Control and Conflicts
406(8)
16.3.1 Transitive Access Control and Relational Information
406(1)
16.3.2 Inconsistency and Reciprocity Conflicts with TAC and RI
407(2)
16.3.3 Formal Definitions
409(5)
16.4 Social Network Construction and Conflict Analysis
414(8)
16.4.1 Constructing the Graph with Tokens for Permissions
414(3)
16.4.2 Relationship Building and Information Discovery in Different Types of Social Networks
417(4)
16.4.3 Comparing Models
421(1)
16.5 Data Mining and Feedback for Awareness Tools
422(5)
16.5.1 Toward Conflict Avoidance and Resolution: Feedback and Trust Mechanisms
425(1)
16.5.2 Desing Choices in Feedback Mechanisms Based on Data Mining
426(1)
16.6 Conclusions and Outlook
427(2)
References
429(4)
17 Privacy Protection of Personal Data in Social Networks
433(26)
Barbara Carminati
Elena Ferrari
Murat Kantarcioglu
Bahvani Thuraisingham
17.1 Introduction
433(1)
17.2 Privacy Issues in Online Social Networks
434(3)
17.3 Access Control for Online Social Networks
437(7)
17.3.1 Challenges in Access Control for Online Social Networks
437(2)
17.3.2 Overview of the Literature
439(2)
17.3.3 Semantic-Based Access Control in Online Social Networks
441(3)
17.4 Privacy Issues in Relationship-Based Access Control Enforcement
444(7)
17.4.1 Challenges in Privacy-Aware Access Control in Online Social Networks
444(2)
17.4.2 Privacy-Aware Access Control in Online Social Networks
446(5)
17.5 Preventing Private Infromation Inference
451(3)
17.5.1 Overview of the Literatures
451(1)
17.5.2 Overview of a Typical Inference Attack on Social Networks
452(2)
17.6 Conclusion and Research Challenges
454(2)
References
456(3)
18 Analyzing Private Network Data
459(40)
Michael Hay
Gerome Miklau
David Jensen
18.1 Introduction
459(5)
18.1.1 How Are Networks Analyzed?
462(1)
18.1.2 Why Should Network Data Be Kept Private?
463(1)
18.1.3 Are Privacy and Utility Compatible?
464(1)
18.1.4 Organization
464(1)
18.2 Attacks on Anonymized Networks
464(11)
18.2.1 Threats: Re-Identification and Edge Disclosure
466(1)
18.2.2 Adversary Knowledge
467(1)
18.2.3 Attacks
468(5)
18.2.4 Attack Effectiveness
473(2)
18.3 Algorithms for Private Data Publication
475(10)
18.3.1 Directed Alteration of Networks
476(3)
18.3.2 Network Generalization
479(4)
18.3.3 Randomly Altering Networks
483(2)
18.4 Algorithms for Private Query Answering
485(7)
18.4.1 Differential Privacy
486(1)
18.4.2 Differential Privacy for Networks
487(1)
18.4.3 Algorithm for Differentially Private Query Answering
488(1)
18.4.4 Network Analysis under Differential Privacy
489(3)
18.5 Conclusion and Future Issues
492(1)
References
493(6)
Index 499
Francesco Bonchi is a senior research scientist at Yahoo! Research in Barcelona, Spain, where he is part of the Barcelona Social Mining Group. He is program co-chair of the upcoming European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010). Dr. Bonchi has also served as program co-chair of the first and second ACM SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007 and 2008), the first IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2006), and the fourth International Workshop on Knowledge Discovery in Inductive Databases (KDID 2005). He earned his Ph.D. in computer science from the University of Pisa.

Elena Ferrari is a professor of computer science at the University of Insubria in Italy, where she heads the Database & Web Security Group. In 2009, Dr. Ferrari received the IEEE Computer Societys prestigious Technical Achievement Award for "outstanding and innovative contributions to secure data management." She has served as program co-chair of the third IFIP WG 11.11 International Conference on Trust Management (IFIPTM 2009), PinKDD 2007 and 2008, the fourth ACM Symposium on Access Control Models and Technologies (SACMAT 2004), and the first Workshop on Web Security and Semantic Web at COMPSAC 2002. She earned her Ph.D. in computer science from the University of Milano. Check out Dr. Ferrari's interview with the IEEE Computer Society.
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