Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

National Institute of Standards and Technology における超伝導研究及び生活

1. Introduction 1.1 An Overview 1.2 Some Examples 1.3 A Brief History 1.4 A Chapter Summary 2. Probability 2.1 Introduction 2.2 Sample Spaces and the Algebra of Sets 2.3 The Probability Function 2.4 Conditional Probability 2.5 Independence 2.6 Combinatorics 2.7 Combinatorial Probability 2.8 Taking a Second Look at Statistics (Monte Carlo Techniques) 3. Random Variables 3.1 Introduction 3.2 Binomial and Hypergeometric Probabilities 3.3 Discrete Random Variables 3.4 Continuous Random Variables 3.5 Expected Values 3.6 The Variance 3.7 Joint Densities 3.8 Transforming and Combining Random Variables 3.9 Further Properties of the Mean and Variance 3.10 Order Statistics 3.11 Conditional Densities 3.12 Moment-Generating Functions 3.13 Taking a Second Look at Statistics (Interpreting Means) Appendix 3.A.1 MINITAB Applications 4. Special Distributions 4.1 Introduction 4.2 The Poisson Distribution 4.3 The Normal Distribution 4.4 The Geometric Distribution 4.5 The Negative Binomial Distribution 4.6 The Gamma Distribution 4.7 Taking a Second Look at Statistics (Monte Carlo Simulations) Appendix 4.A.1 MINITAB Applications Appendix 4.A.2 A Proof of the Central Limit Theorem 5. Estimation 5.1 Introduction 5.2 Estimating Parameters: The Method of Maximum Likelihood and the Method of Moments 5.3 Interval Estimation 5.4 Properties of Estimators 5.5 Minimum-Variance Estimators: The Crami?½r-Rao Lower Bound 5.6 Sufficient Estimators 5.7 Consistency 5.8 Bayesian Estimation 5.9 Taking A Second Look at Statistics (Beyond Classical Estimation) Appendix 5.A.1 MINITAB Applications 6. Hypothesis Testing 6.1 Introduction 6.2 The Decision Rule 6.3 Testing Binomial Dataâ H0: p = po 6.4 Type I and Type II Errors 6.5 A Notion of Optimality: The Generalized Likelihood Ratio 6.6 Taking a Second Look at Statistics (Statistical Significance versus â Practicalâ Significance) 7. Inferences Based on the Normal Distribution 7.1 Introduction 7.2 Comparing Y-i?½ s/ vn and Y-i?½ S/ vn 7.3 Deriving the Distribution of Y-i?½ S/ vn 7.4 Drawing Inferences About i?½ 7.5 Drawing Inferences About s2 7.6 Taking a Second Look at Statistics (Type II Error) Appendix 7.A.1 MINITAB Applications Appendix 7.A.2 Some Distribution Results for Y and S2 Appendix 7.A.3 A Proof that the One-Sample t Test is a GLRT Appendix 7.A.4 A Proof of Theorem 7.5.2 8. Types of Data: A Brief Overview 8.1 Introduction 8.2 Classifying Data 8.3 Taking a Second Look at Statistics (Samples Are Not â Validâ !) 9. Two-Sample Inferences 9.1 Introduction 9.2 Testing H0: i?½X =i?½Y 9.3 Testing H0: s2X=s2Yâ The F Test 9.4 Binomial Data: Testing H0: pX = pY 9.5 Confidence Intervals for the Two-Sample Problem 9.6 Taking a Second Look at Statistics (Choosing Samples) Appendix 9.A.1 A Derivation of the Two-Sample t Test (A Proof of Theorem 9.2.2) Appendix 9.A.2 MINITAB Applications 10. Goodness-of-Fit Tests 10.1 Introduction 10.2 The Multinomial Distribution 10.3 Goodness-of-Fit Tests: All Parameters Known 10.4 Goodness-of-Fit Tests: Parameters Unknown 10.5 Contingency Tables 10.6 Taking a Second Look at Statistics (Outliers) Appendix 10.A.1 MINITAB Applications 11. Regression 11.1 Introduction 11.2 The Method of Least Squares 11.3 The Linear Model 11.4 Covariance and Correlation 11.5 The Bivariate Normal Distribution 11.6 Taking a Second Look at Statistics (How Not to Interpret the Sample Correlation Coefficient) Appendix 11.A.1 MINITAB Applications Appendix 11.A.2 A Proof of Theorem 11.3.3 12. The Analysis of Variance 12.1 Introduction 12.2 The F Test 12.3 Multiple Comparisons: Tukeyâ s Method 12.4 Testing Subhypotheses with Contrasts 12.5 Data Transformations 12.6 Taking a Second Look at Statistics (Putting the Subject of Statistics togetherâ the Contributions of Ronald A. Fisher) Appendix 12.A.1 MINITAB Applications Appendix 12.A.2 A Proof of Theorem 12.2.2 Appendix 12.A.3 The Distribution of SSTR/(kâ 1) SSE/(nâ k)When H1 is True 13. Randomized Block Designs 13.1 Introduction 13.2 The F Test for a Randomized Block Design 13.3 The Paired t Test 13.4 Taking a Second Look at Statistics (Choosing between a Two-Sample t Test and a Paired t Test) Appendix 13.A.1 MINITAB Applications 14. Nonparametric Statistics 14.1 Introduction 14.2 The Sign Test 14.3 Wilcoxon Tests 14.4 The Kruskal-Wallis Test 14.5 The Friedman Test 14.6 Testing for Randomness 14.7 Taking a Second Look at Statistics (Comparing Parametric and Nonparametric Procedures) Appendix 14.A.1 MINITAB Applications Appendix: Statistical Tables Answers to Selected Odd-Numbered Questions Bibliography Index

http://library.um.ac.id/free-contents/downloadpdf.php/buku/an-introduction-to-mathematical-statistics-and-its-applications-richard-j-larsen-morris-l-marx-12385.pdf

An introduction to mathematical statistical and its applications / Richard J. Larsen, Morris L. Marx

Safety-critical applications in cooperative vehicular networks require authentication of nodes and messages. Yet, privacy of individual vehicles and drivers must be maintained. Pseudonymity can satisfy both security and privacy requirements. Thus, a large body of work emerged in recent years, proposing pseudonym solutions tailored to vehicular networks. In this survey, we detail the challenges and requirements for such pseudonym mechanisms, propose an abstract pseudonym lifecycle, and give an extensive overview and categorization of the state of the art in this research area. Specifically, this survey covers pseudonym schemes based on public key and identity-based cryptography, group signatures and symmetric authentication. We compare the different approaches, give an overview of the current state of standardization, and identify open research challenges.

Pseudonym Schemes in Vehicular Networks: A Survey

This paper critically examines some recently proposed RFID privacy models. It shows that some models suffer from weaknesses such as insufficient generality and unrealistic assumptions regarding the adversary's ability to corrupt tags. We propose a new RFID privacy model that is based on the notion of indistinguishability and that does not suffer from the identified drawbacks. We demonstrate the easy applicability of our model by applying it to multiple existing RFID protocols.

/pdf/a-new-rfid-privacy-model-201q5gnyzj.pdf

A new RFID privacy model

https://lirias.kuleuven.be/bitstream/123456789/309465/2/article-2028.pdf

A New RFID Privacy Model

At present, network users have to remember a username and a corresponding password for every service with which they are registered. One solution to the security and usability implications of this situation is Single Sign-On, whereby the user authenticates only once to an ‘Authentication Service Provider’ (ASP) and subsequently uses disparate Service Providers (SPs) without necessarily re-authenticating. The information about the user’s authentication status is handled between the ASP and the desired SP transparently to the user. This paper describes a method by which the end-user’s computing platform itself plays the role of the ASP. The platform has to be a Trusted Platform conforming to the Trusted Computing Platform Alliance (TCPA) specifications. The relevant TCPA architectural components and security services are described and associated threats are analysed.

/pdf/single-sign-on-using-trusted-platforms-1gcu07oc08.pdf

Single Sign-On Using Trusted Platforms

Secret handshake (SH) schemes enable two members who belong to the same group to authenticate each other in a way that hides their affiliation to that group from all others. In previous works, the group authority (GA) has the ability to reveal the identity (ID) of a handshake player who belongs to his group. In this paper, we focus first on the classification of traceability of GA. We classify this feature as follows: (i) GA of G is able to reveal IDs of members belonging to G by using a transcript of a handshake protocol; (ii) GA of G is able to confirm whether handshake players belong to G or not by using a transcript of a handshake protocol. In some situations, only the latter capability is needed. So, we consider a SH that GA has only an ability to confirm whether a handshake player belongs to his own group without revealing his ID. Thus, we introduce a SH scheme with request-based-revealing (SHRBR). In SHRBR, GA can check whether handshake players belong to the own group without revealing a member ID. After a handshake player A executes a handshake protocol with B, if A wants to reveal a handshake partner (in this case B), A requests GA to reveal a handshake partner’s ID by bringing forth his own ID and secret information. We define the security requirements for SHRBR and propose a concrete SHRBR in the random oracle model.

Public Key Infrastructures, Services and Applications

This article presents a hierarchy of privacy notions that covers multiple anonymity and unlinkability variants. The underlying definitions, which are based on the idea of indistinguishability between two worlds, provide new insights into the relation between, and the fundamental structure of, different privacy notions. We furthermore place previous privacy definitions concerning group signature, anonymous communication, and secret voting systems in the context of our hierarchy; this renders these traditionally disconnected notions comparable.

/pdf/relations-among-privacy-notions-mruu6qfxqz.pdf

Andreas Pashalidis

Papers

A new RFID privacy model

A New RFID Privacy Model

Single Sign-On Using Trusted Platforms

Public Key Infrastructures, Services and Applications

Relations among privacy notions