Data Mining - Concepts and Techniques.

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

Cloud storage as one of the most important cloud services enables cloud users to save more data without enlarging its own storage. In order to eliminate repeated data and improve the utilization of storage, deduplication is employed to cloud storage. Due to the concern about data security and user privacy, encryption is introduced, but incurs new challenge to cloud data deduplication. Existing work cannot achieve flexible access control and user revocation. Moreover, few of them can support efficient ownership proof, especially public verifiability of ownership. In this paper, we propose a secure encrypted data deduplication scheme with effective ownership proof and user revocation. We evaluate its performance and prove its security. The simulation results show that our scheme is efficient and effective for potential practical employment.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4790&context=sis_research

Secure encrypted data deduplication with ownership proof and user revocation

Trusted Computing (TC) is a technology developed and promoted by the Trusted Computing Group (TCG), and aims to enhance the overall security, privacy and trustworthiness of a variety of computing devices. The core of the trusted computing technologies of TCG is the Trusted Platform Module (TPM), a tamper-resistant module embedded in a platform, which is implemented by a TPM chip and designed to resist software attacks and moderate hardware attacks. TPM includes non-volatile storage, a set of Platform Configuration Registers (PCRs) and an engine for cryptographic operations. Based on TPM and TCG Software Stack (TSS), a suite of mechanisms can be realized, including memory curtaining, protected execution, secure I/O, sealed storage, platform measurement and remote attestation. As a fundamental secure computing module, TPM is the root trust module to establish security and trust in digital computing, communication and networking. Basically, it can support secure booting and trustworthy software installation, private data protection and Digital Rights Management (DRM), as well as remote attestation- based trusted communications and collaboration. There is significant research currently in the field of trusted computing, in both academia and industry. Recent efforts focus on TC module development and extension, trustworthy software assurance, Trusted Execution Environment (TEE), trusted collaboration and trusted storage. The recent advance of Intel Software Guard Extensions (Intel® SGX) offers hardware-assistant TEE, which allows user code execution in a private region of memory, called enclave, in an untrusted third party. This technology attracts special attention and motivates innovations of many applications.

/pdf/ieee-access-special-section-editorial-trusted-computing-welmbbfldw.pdf

IEEE Access Special Section Editorial: Trusted Computing

Device-to-Device (D2D) communication is proposed as a promising technology in 5G system for communications between mobile devices geographical proximity. Despite significant benefits, new application scenarios and system architecture, for instance, open direct channel, expose D2D communications to unique security threats. Providing a secure and privacy-preserving D2D communication system is essential for the success of D2D services. In this paper, we propose AAKA-D2D, an anonymous authentication and key agreement secure protocol for D2D communications, by which two User Equipments (UE) in close proximity could mutually authenticate without leaking their real identities and negotiate a session key for secure communications in D2D session without disclosing communication contents to Core Network (CN). Formal security analysis indicates that AAKA-D2D satisfies the security requirements. The comprehensive performance evaluation show that AAKA-D2D can increase the computational performance by about 20% and decrease on communication overhead by half compared to related works.

/pdf/aaka-d2d-anonymous-authentication-and-key-agreement-protocol-23piotvn9q.pdf

AAKA-D2D: Anonymous Authentication and Key Agreement Protocol in D2D Communications

A mobile application is a software package that can be installed and executed on a mobile device. Which mobile application is more trustworthy for a user to purchase, download, install, consume or recommend becomes a crucial issue that impacts its final success. This paper explores trust of mobile applica- tions based on users' behaviors. It proposes a conceptual trust model according to trust behavior study. We achieve our model through Principal Component Analysis (PCA), reliability analysis, Confirmatory Factor Analysis (CFA) based on data collected from a questionnaire survey. It is indicated that a user's trust behavior is a multidimensional construct composed of three main aspects: using behavior, reflection behavior, and correlation behavior. They are further deline- ated into twelve measurable sub-constructs and relate to a number of external factors. The data analysis showed that the questionnaire has positive psychomet- ric properties with respect to model construct validity and reliability. Particu- larly, the practical significance of our work towards usable trust management, the limitations of current empirical study and future work are also discussed.

Exploring Trust of Mobile Applications Based on User Behaviors (Work-in-Progress)

Providing a trustworthy mobile computing platform is crucial for mobile communications, services and applications. This chapter studies methodologies and mechanisms of providing a trustworthy computing platform for mobile devices. In addition, we seek solutions to support trusted communications and collaboration among those platforms in a distributed and dynamic system. The first part of this chapter gives a brief overview of literature background. It includes detailed state-of-the-art in conceptualizing trust, trust modeling, trust evaluation and trust management and identifies emerging trends in this area. The second part of this chapter specifies a mechanism for trust sustainability among the platforms based on a trusted computing technology. It plays as the first level of autonomic trust management in our solution. The third part describes an adaptive trust control model. The trust management mechanism based on this model plays as the second level of our autonomic trust management solution. We demonstrate how the above two mechanisms can cooperate together to provide a comprehensive solution in the forth part. The fifth part further discusses other related issues, such as standardization and implementation strategies. Finally, conclusions and future work are presented in the last part.

Zheng Yan

Papers

Secure encrypted data deduplication with ownership proof and user revocation

IEEE Access Special Section Editorial: Trusted Computing

AAKA-D2D: Anonymous Authentication and Key Agreement Protocol in D2D Communications

Exploring Trust of Mobile Applications Based on User Behaviors (Work-in-Progress)

Security via Trusted Communications