| Ubiquitous/Pervasive computing system comprises heterogeneous computing devices that are'invisibly'embedded into environment and provide users with ubiquitous access to services. For using these services, ubiquitous computing devices may form context-aware networks for capturing contexts about users. Such contexts can be used by Ubicomp system to adapt its functionality and behavior to various user preferences. This means pervasive computing system may facilitate unobtrusive access, manipulation, and presentation of personal data derived from contexts. The unobtrusive features of ubiquitous computing may foster unethical use of the technology but, more significantly, they are also much more conducive to inadvertent intrusions on privacy.Privacy is the claim of individuals to determine for themselves when, how, and to what extent information about them is communicated to others. Accordingly, this paper address privacy issues by enabling individual (policy-author) to make privacy policies for controlling personal data. In such a case, entity (individual or agent) can access policy-author's personal data only if permitted by her privacy policy. This paper focuses on the representation and reasoning of user privacy policy both in the level of abstract model and application frame. The main contents and innovations in this paper are summarized such as:(1) Access control mechanism for pervasive computing is described firstly. Then, privacy policy formalization is studyed based on predicate logic for pervasive computing. That is, any user privacy policy described in natural language can be formalized as predicate logic formula by extracting the hidden restrictions in context of pervasive computing. Accordingly, this paper depicts the decomposition of privacy policy, and proposes two novel concepts: primitive privacy policy and executive privacy policy. Besides, the alphabet table, item and formula in privacy policy formalization system are defined. The pervasive computing application system is abstracted into a mathematical structure convenient for formal analysis, and the explanation and semantic of privacy policy formula are put forward.(2) Privacy policy model based on first-order logic is introduced to uniform the privacy policy primitive which is the essential element constructing the privacy policy and provides consistent research object and privacy policy semantic for following research in this paper. Many-sorted logic is introduced and the necessity and importance of adopting it are pointed out as well. Through the analysis to privacy policy sample described in natural language, various primitive which construct privacy policy are regarded as sort in many-sorted logic. Also, the executive environment model is presented and executive privacy policy model is concluded based on it.(3) Description logic theory is introduced, which can be used to represent and reason domain knowledge based on term and assertion. Combined description logic and the privacy policy primitive proposed by this paper, a privacy policy knowledge base PKB (TBox, ABox) including the abstract model of pervasive computing application structure and the privacy policy with the form of individual assertion is established. Besides, this paper designs the axioms for user group and privacy policy in TBox and individual assertions in ABox. At the same time, it points out that privacy policy can be expressed by privacy policy axioms. In addition, grounded on the individual assertions about users, user properties, relationships in ABox, the concept of privacy rules knowledge base PRKB (TBox, ABox, RBox) is put forward and the formalization reasoning process of privacy policy is analyzed.(4) After introduction of Web Ontology Language and Rules Engine, both privacy policy expression method based on ontology and executive mechanism based on rules in application domain are discussed. Application frame of privacy policy in pervasive computing is proposed based on the verifying of privacy policy executive mechanism. In ontology expression side, ontology of general rules is defined firstly, followed the ontology of privacy rules. At the same time, the requests to privacy information is regarded as rules with out prerequisite to define their ontology. The general expression of privacy policy based on ontology is presented from a relatively abstract and general level. Additionally, a proper improvement is designed to add a function to control the granularity of privacy information. In rules reasoning side, according to the policy primitive, the expression method of privacy policy rules is presented, and layered implementation environment and corresponding mapping rules are defined. The procedure of response for incoming query is analyzed and substantiated by experiments. At the basis of above expression and reasoning of privacy policy basded on ontology, this paper proposed a suggestive application frame of privacy policy in pervasive computing from model and frame level, the practicality of which is analyzed.(5) Based on user context information and the ability of CBR (Case-based Reasoning), this paper gives a preliminary study on learning context-sensitive privacy policies. Firstly, the user context for pervasive computing environment is introduced, which, in this paper, is grouped into two categories, one is static user context information, and the other is dynamic user context information. This paper argues that user context information can be used to support the dynamic generating of privacy policies in a user interface for reducing the burden of user specifying policies. A history privacy policy can be regarded as a case of CBR system, and stored into the case base. Following this basic notion, this paper presents an abstract case representation based on policy primitives, where any privacy policy case is represented as a feature-value vector. For indexing privacy policy cases, this paper chooses the requester of privacy information as key index and forms the structure of case base. Finally, case retrieval algorithm for privacy policy case base is introduced based on the core of similarity measure in this paper.
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