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Author Li, Ninghui.
Title Differential privacy [electronic resource] : from theory to practice / Ninghui Li, Min Lyu, Dong Su, Weining Yang.
Publication Info [San Rafael, California] : Morgan & Claypool, 2017.



Descript 1 PDF (xiii, 124 pages)
Note Part of: Synthesis digital library of engineering and computer science.
Contents 1. Introduction -- 1.1 Privacy violation incidents -- 1.1.1 Privacy incidents -- 1.1.2 Lessons from privacy incidents -- 1.2 On balancing theory and practice -- 1.3 Organization of this book -- 1.4 Topics for volume 2 --
2. A primer on [epsilon]-differential privacy -- 2.1 The definition of [epsilon]-DP -- 2.1.1 Bounded DP or unbounded DP -- 2.2 Properties of [epsilon]-DP -- 2.2.1 Post-processing and sequential composition -- 2.2.2 Parallel composition and convexity -- 2.3 The Laplace mechanism -- 2.3.1 The scalar case -- 2.3.2 The vector case -- 2.4 The exponential mechanism -- 2.4.1 The general case of the exponential mechanism -- 2.4.2 The monotonic case of the exponential mechanism -- 2.4.3 Case study: computing mode and median -- 2.4.4 Discussion on the exponential mechanism -- 2.5 Case study: computing average -- 2.5.1 Applying the Laplace and the exponential mechanism -- 2.5.2 Applying the Laplace mechanism and composition -- 2.5.3 A non-private average algorithm using accurate count -- 2.5.4 NoisyAverage with accurate count -- 2.5.5 NoisyAverage with normalization -- 2.5.6 Which is best -- 2.6 Settings to apply DP -- 2.7 Bibliographical notes --
3. What does DP mean? -- 3.1 Limitations of syntactic notions -- 3.2 Semantic guarantees of differential privacy -- 3.2.1 Infeasibility of achieving "privacy as secrecy" -- 3.2.2 Toward a "real-world-ideal-world" approach -- 3.2.3 DP as approximating the ideal world of "privacy as control" -- 3.2.4 A formulation of DP's semantic guarantee -- 3.2.5 The personal data principle -- 3.2.6 A case study in applying PDP -- 3.3 Examining DP and PDP -- 3.3.1 When the notion of neighboring datasets is defined incorrectly -- 3.3.2 When using DP in the local setting -- 3.3.3 What constitutes one individual's data -- 3.3.4 An individual's personal data or personal data under one individual's control -- 3.3.5 Group privacy as a potential legal Achilles' heel for DP -- 3.3.6 A moral challenge to private party benefiting from DP -- 3.4 Additional caveats when using DP -- 3.4.1 Using an [epsilon] that is too large -- 3.4.2 Applying a model to personal data -- 3.4.3 Privacy and discrimination -- 3.5 Bibliographical notes --
4. Publishing histograms for low-dimensional datasets -- 4.1 Problem definition -- 4.1.1 Three settings -- 4.1.2 Measuring utility -- 4.2 Dense pre-defined partitioning -- 4.2.1 The baseline: a simple histogram -- 4.2.2 The hierarchical method -- 4.2.3 Constrained inference -- 4.2.4 Effect of privacy budget allocation in hierarchical histograms -- 4.2.5 Wavelet transforms and other optimizations -- 4.2.6 Beyond one-dimensional datasets -- 4.3 Lacking suitable partitioning -- 4.3.1 The uniform grid method--UG -- 4.3.2 The adaptive grids approach--AG, 2D case -- 4.3.3 Bottom-up grouping -- 4.3.4 Recursive partitioning -- 4.4 Bibliographical notes --
5. Differentially private optimization -- 5.1 Example optimization problems -- 5.1.1 k-means clustering -- 5.1.2 Linear regression -- 5.1.3 Logistic regression -- 5.1.4 SVM -- 5.2 Objective perturbation -- 5.2.1 Adding a noisy linear term to the optimization objective function -- 5.2.2 The functional mechanism -- 5.3 Make an existing algorithm private -- 5.3.1 DPLloyd: differentially private Lloyd algorithm for k-means clustering -- 5.3.2 DiffPID3: differential private ID3 algorithm for decision tree classification -- 5.4 Iterative local search via EM -- 5.4.1 PrivGene: differentially private model fitting using genetic algorithms -- 5.4.2 Iterative local search -- 5.4.3 Enhanced exponential mechanism -- 5.5 Histograms optimized for optimization -- 5.5.1 Uniform grid and its extensions -- 5.5.2 Histogram publishing for estimating M-estimators -- 5.5.3 DiffGen: differentially private anonymization based on generalization -- 5.5.4 PrivPfC: differentially private data publication for classification -- 5.6 Bibliographical notes --
6. Publishing marginals -- 6.1 Problem definition -- 6.2 Methods that don't fit the problem -- 6.2.1 The flat method -- 6.2.2 The direct method -- 6.2.3 Adding noise in the Fourier domain -- 6.2.4 Data cubes -- 6.2.5 Multiplicative weights mechanism -- 6.2.6 Learning based approaches -- 6.3 The PriView approach -- 6.3.1 Summary of the PriView approach -- 6.3.2 Computing k-way marginals -- 6.3.3 Consistency between noisy views -- 6.3.4 Choosing a set of views -- 6.3.5 Space and time complexity -- 6.4 Bibliographical notes --
7. The sparse vector technique -- 7.1 Introduction -- 7.2 Variants of SVT -- 7.2.1 Privacy proof for proposed SVT -- 7.2.2 Privacy properties of other variants -- 7.2.3 Error in privacy analysis of GPTT -- 7.2.4 Other variants -- 7.3 Optimizing SVT -- 7.3.1 A generalized SVT algorithm -- 7.3.2 Optimizing privacy budget allocation -- 7.3.3 SVT for monotonic queries -- 7.4 SVT vs. EM -- 7.4.1 Evaluation -- 7.5 Bibliographical notes -- Bibliography -- Authors' biographies.
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
.
ISBN 9781627052979 ebook
9781627054935 print
Standard # 10.2200/S00735ED1V01Y201609SPT018 doi
Click on the terms below to find similar items in the catalogue
Author Li, Ninghui.
Series Synthesis lectures on information security, privacy, and trust, # 18
Synthesis digital library of engineering and computer science.
Synthesis lectures on information security, privacy, and trust ; # 18. 1945-9750
Subject Privacy -- Mathematical models.
Alt author Lyu, Min.
Su, Dong.
Yang, Weining.
Descript 1 PDF (xiii, 124 pages)
Note Part of: Synthesis digital library of engineering and computer science.
Contents 1. Introduction -- 1.1 Privacy violation incidents -- 1.1.1 Privacy incidents -- 1.1.2 Lessons from privacy incidents -- 1.2 On balancing theory and practice -- 1.3 Organization of this book -- 1.4 Topics for volume 2 --
2. A primer on [epsilon]-differential privacy -- 2.1 The definition of [epsilon]-DP -- 2.1.1 Bounded DP or unbounded DP -- 2.2 Properties of [epsilon]-DP -- 2.2.1 Post-processing and sequential composition -- 2.2.2 Parallel composition and convexity -- 2.3 The Laplace mechanism -- 2.3.1 The scalar case -- 2.3.2 The vector case -- 2.4 The exponential mechanism -- 2.4.1 The general case of the exponential mechanism -- 2.4.2 The monotonic case of the exponential mechanism -- 2.4.3 Case study: computing mode and median -- 2.4.4 Discussion on the exponential mechanism -- 2.5 Case study: computing average -- 2.5.1 Applying the Laplace and the exponential mechanism -- 2.5.2 Applying the Laplace mechanism and composition -- 2.5.3 A non-private average algorithm using accurate count -- 2.5.4 NoisyAverage with accurate count -- 2.5.5 NoisyAverage with normalization -- 2.5.6 Which is best -- 2.6 Settings to apply DP -- 2.7 Bibliographical notes --
3. What does DP mean? -- 3.1 Limitations of syntactic notions -- 3.2 Semantic guarantees of differential privacy -- 3.2.1 Infeasibility of achieving "privacy as secrecy" -- 3.2.2 Toward a "real-world-ideal-world" approach -- 3.2.3 DP as approximating the ideal world of "privacy as control" -- 3.2.4 A formulation of DP's semantic guarantee -- 3.2.5 The personal data principle -- 3.2.6 A case study in applying PDP -- 3.3 Examining DP and PDP -- 3.3.1 When the notion of neighboring datasets is defined incorrectly -- 3.3.2 When using DP in the local setting -- 3.3.3 What constitutes one individual's data -- 3.3.4 An individual's personal data or personal data under one individual's control -- 3.3.5 Group privacy as a potential legal Achilles' heel for DP -- 3.3.6 A moral challenge to private party benefiting from DP -- 3.4 Additional caveats when using DP -- 3.4.1 Using an [epsilon] that is too large -- 3.4.2 Applying a model to personal data -- 3.4.3 Privacy and discrimination -- 3.5 Bibliographical notes --
4. Publishing histograms for low-dimensional datasets -- 4.1 Problem definition -- 4.1.1 Three settings -- 4.1.2 Measuring utility -- 4.2 Dense pre-defined partitioning -- 4.2.1 The baseline: a simple histogram -- 4.2.2 The hierarchical method -- 4.2.3 Constrained inference -- 4.2.4 Effect of privacy budget allocation in hierarchical histograms -- 4.2.5 Wavelet transforms and other optimizations -- 4.2.6 Beyond one-dimensional datasets -- 4.3 Lacking suitable partitioning -- 4.3.1 The uniform grid method--UG -- 4.3.2 The adaptive grids approach--AG, 2D case -- 4.3.3 Bottom-up grouping -- 4.3.4 Recursive partitioning -- 4.4 Bibliographical notes --
5. Differentially private optimization -- 5.1 Example optimization problems -- 5.1.1 k-means clustering -- 5.1.2 Linear regression -- 5.1.3 Logistic regression -- 5.1.4 SVM -- 5.2 Objective perturbation -- 5.2.1 Adding a noisy linear term to the optimization objective function -- 5.2.2 The functional mechanism -- 5.3 Make an existing algorithm private -- 5.3.1 DPLloyd: differentially private Lloyd algorithm for k-means clustering -- 5.3.2 DiffPID3: differential private ID3 algorithm for decision tree classification -- 5.4 Iterative local search via EM -- 5.4.1 PrivGene: differentially private model fitting using genetic algorithms -- 5.4.2 Iterative local search -- 5.4.3 Enhanced exponential mechanism -- 5.5 Histograms optimized for optimization -- 5.5.1 Uniform grid and its extensions -- 5.5.2 Histogram publishing for estimating M-estimators -- 5.5.3 DiffGen: differentially private anonymization based on generalization -- 5.5.4 PrivPfC: differentially private data publication for classification -- 5.6 Bibliographical notes --
6. Publishing marginals -- 6.1 Problem definition -- 6.2 Methods that don't fit the problem -- 6.2.1 The flat method -- 6.2.2 The direct method -- 6.2.3 Adding noise in the Fourier domain -- 6.2.4 Data cubes -- 6.2.5 Multiplicative weights mechanism -- 6.2.6 Learning based approaches -- 6.3 The PriView approach -- 6.3.1 Summary of the PriView approach -- 6.3.2 Computing k-way marginals -- 6.3.3 Consistency between noisy views -- 6.3.4 Choosing a set of views -- 6.3.5 Space and time complexity -- 6.4 Bibliographical notes --
7. The sparse vector technique -- 7.1 Introduction -- 7.2 Variants of SVT -- 7.2.1 Privacy proof for proposed SVT -- 7.2.2 Privacy properties of other variants -- 7.2.3 Error in privacy analysis of GPTT -- 7.2.4 Other variants -- 7.3 Optimizing SVT -- 7.3.1 A generalized SVT algorithm -- 7.3.2 Optimizing privacy budget allocation -- 7.3.3 SVT for monotonic queries -- 7.4 SVT vs. EM -- 7.4.1 Evaluation -- 7.5 Bibliographical notes -- Bibliography -- Authors' biographies.
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
.
ISBN 9781627052979 ebook
9781627054935 print
Standard # 10.2200/S00735ED1V01Y201609SPT018 doi
Author Li, Ninghui.
Series Synthesis lectures on information security, privacy, and trust, # 18
Synthesis digital library of engineering and computer science.
Synthesis lectures on information security, privacy, and trust ; # 18. 1945-9750
Subject Privacy -- Mathematical models.
Alt author Lyu, Min.
Su, Dong.
Yang, Weining.

Subject Privacy -- Mathematical models.
Descript 1 PDF (xiii, 124 pages)
Note Part of: Synthesis digital library of engineering and computer science.
Contents 1. Introduction -- 1.1 Privacy violation incidents -- 1.1.1 Privacy incidents -- 1.1.2 Lessons from privacy incidents -- 1.2 On balancing theory and practice -- 1.3 Organization of this book -- 1.4 Topics for volume 2 --
2. A primer on [epsilon]-differential privacy -- 2.1 The definition of [epsilon]-DP -- 2.1.1 Bounded DP or unbounded DP -- 2.2 Properties of [epsilon]-DP -- 2.2.1 Post-processing and sequential composition -- 2.2.2 Parallel composition and convexity -- 2.3 The Laplace mechanism -- 2.3.1 The scalar case -- 2.3.2 The vector case -- 2.4 The exponential mechanism -- 2.4.1 The general case of the exponential mechanism -- 2.4.2 The monotonic case of the exponential mechanism -- 2.4.3 Case study: computing mode and median -- 2.4.4 Discussion on the exponential mechanism -- 2.5 Case study: computing average -- 2.5.1 Applying the Laplace and the exponential mechanism -- 2.5.2 Applying the Laplace mechanism and composition -- 2.5.3 A non-private average algorithm using accurate count -- 2.5.4 NoisyAverage with accurate count -- 2.5.5 NoisyAverage with normalization -- 2.5.6 Which is best -- 2.6 Settings to apply DP -- 2.7 Bibliographical notes --
3. What does DP mean? -- 3.1 Limitations of syntactic notions -- 3.2 Semantic guarantees of differential privacy -- 3.2.1 Infeasibility of achieving "privacy as secrecy" -- 3.2.2 Toward a "real-world-ideal-world" approach -- 3.2.3 DP as approximating the ideal world of "privacy as control" -- 3.2.4 A formulation of DP's semantic guarantee -- 3.2.5 The personal data principle -- 3.2.6 A case study in applying PDP -- 3.3 Examining DP and PDP -- 3.3.1 When the notion of neighboring datasets is defined incorrectly -- 3.3.2 When using DP in the local setting -- 3.3.3 What constitutes one individual's data -- 3.3.4 An individual's personal data or personal data under one individual's control -- 3.3.5 Group privacy as a potential legal Achilles' heel for DP -- 3.3.6 A moral challenge to private party benefiting from DP -- 3.4 Additional caveats when using DP -- 3.4.1 Using an [epsilon] that is too large -- 3.4.2 Applying a model to personal data -- 3.4.3 Privacy and discrimination -- 3.5 Bibliographical notes --
4. Publishing histograms for low-dimensional datasets -- 4.1 Problem definition -- 4.1.1 Three settings -- 4.1.2 Measuring utility -- 4.2 Dense pre-defined partitioning -- 4.2.1 The baseline: a simple histogram -- 4.2.2 The hierarchical method -- 4.2.3 Constrained inference -- 4.2.4 Effect of privacy budget allocation in hierarchical histograms -- 4.2.5 Wavelet transforms and other optimizations -- 4.2.6 Beyond one-dimensional datasets -- 4.3 Lacking suitable partitioning -- 4.3.1 The uniform grid method--UG -- 4.3.2 The adaptive grids approach--AG, 2D case -- 4.3.3 Bottom-up grouping -- 4.3.4 Recursive partitioning -- 4.4 Bibliographical notes --
5. Differentially private optimization -- 5.1 Example optimization problems -- 5.1.1 k-means clustering -- 5.1.2 Linear regression -- 5.1.3 Logistic regression -- 5.1.4 SVM -- 5.2 Objective perturbation -- 5.2.1 Adding a noisy linear term to the optimization objective function -- 5.2.2 The functional mechanism -- 5.3 Make an existing algorithm private -- 5.3.1 DPLloyd: differentially private Lloyd algorithm for k-means clustering -- 5.3.2 DiffPID3: differential private ID3 algorithm for decision tree classification -- 5.4 Iterative local search via EM -- 5.4.1 PrivGene: differentially private model fitting using genetic algorithms -- 5.4.2 Iterative local search -- 5.4.3 Enhanced exponential mechanism -- 5.5 Histograms optimized for optimization -- 5.5.1 Uniform grid and its extensions -- 5.5.2 Histogram publishing for estimating M-estimators -- 5.5.3 DiffGen: differentially private anonymization based on generalization -- 5.5.4 PrivPfC: differentially private data publication for classification -- 5.6 Bibliographical notes --
6. Publishing marginals -- 6.1 Problem definition -- 6.2 Methods that don't fit the problem -- 6.2.1 The flat method -- 6.2.2 The direct method -- 6.2.3 Adding noise in the Fourier domain -- 6.2.4 Data cubes -- 6.2.5 Multiplicative weights mechanism -- 6.2.6 Learning based approaches -- 6.3 The PriView approach -- 6.3.1 Summary of the PriView approach -- 6.3.2 Computing k-way marginals -- 6.3.3 Consistency between noisy views -- 6.3.4 Choosing a set of views -- 6.3.5 Space and time complexity -- 6.4 Bibliographical notes --
7. The sparse vector technique -- 7.1 Introduction -- 7.2 Variants of SVT -- 7.2.1 Privacy proof for proposed SVT -- 7.2.2 Privacy properties of other variants -- 7.2.3 Error in privacy analysis of GPTT -- 7.2.4 Other variants -- 7.3 Optimizing SVT -- 7.3.1 A generalized SVT algorithm -- 7.3.2 Optimizing privacy budget allocation -- 7.3.3 SVT for monotonic queries -- 7.4 SVT vs. EM -- 7.4.1 Evaluation -- 7.5 Bibliographical notes -- Bibliography -- Authors' biographies.
Note Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
.
Alt author Lyu, Min.
Su, Dong.
Yang, Weining.
ISBN 9781627052979 ebook
9781627054935 print
Standard # 10.2200/S00735ED1V01Y201609SPT018 doi

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