4 research outputs found

    Access Control Administration with Adjustable Decentralization

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    Access control is a key function of enterprises that preserve and propagate massive data. Access control enforcement and administration are two major components of the system. On one hand, enterprises are responsible for data security; thus, consistent and reliable access control enforcement is necessary although the data may be distributed. On the other hand, data often belongs to several organizational units with various access control policies and many users; therefore, decentralized administration is needed to accommodate diverse access control needs and to avoid the central bottleneck. Yet, the required degree of decentralization varies within different organizations: some organizations may require a powerful administrator in the system; whereas, some others may prefer a self-governing setting in which no central administrator exists, but users fully manage their own data. Hence, a single system with adjustable decentralization will be useful for supporting various (de)centralized models within the spectrum of access control administration. Giving individual users the ability to delegate or grant privileges is a means of decentralizing access control administration. Revocation of arbitrary privileges is a means of retaining control over data. To provide flexible administration, the ability to delegate a specific privilege and the ability to revoke it should be held independently of each other and independently of the privilege itself. Moreover, supporting arbitrary user and data hierarchies, fine-grained access control, and protection of both data (end objects) and metadata (access control data) with a single uniform model will provide the most widely deployable access control system. Conflict resolution is a major aspect of access control administration in systems. Resolving access conflicts when deriving effective privileges from explicit ones is a challenging problem in the presence of both positive and negative privileges, sophisticated data hierarchies, and diversity of conflict resolution strategies. This thesis presents a uniform access control administration model with adjustable decentralization, to protect both data and metadata. There are several contributions in this work. First, we present a novel mechanism to constrain access control administration for each object type at object creation time, as a means of adjusting the degree of decentralization for the object when the system is configured. Second, by controlling the access control metadata with the same mechanism that controls the users’ data, privileges can be granted and revoked to the extent that these actions conform to the corporation’s access control policy. Thus, this model supports a whole spectrum of access control administration, in which each model is characterized as a network of access control states, similar to a finite state automaton. The model depends on a hierarchy of access banks of authorizations which is supported by a formal semantics. Within this framework, we also introduce the self-governance property in the context of access control, and show how the model facilitates it. In particular, using this model, we introduce a conflict-free and decentralized access control administration model in which all users are able to retain complete control over their own data while they are also able to delegate any subset of their privileges to other users or user groups. We also introduce two measures to compare any two access control models in terms of the degrees of decentralization and interpretation. Finally, as the conflict resolution component of access control models, we incorporate a unified algorithm to resolve access conflicts by simultaneously supporting several combined strategies

    Query Evaluation in the Presence of Fine-grained Access Control

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    Access controls are mechanisms to enhance security by protecting data from unauthorized accesses. In contrast to traditional access controls that grant access rights at the granularity of the whole tables or views, fine-grained access controls specify access controls at finer granularity, e.g., individual nodes in XML databases and individual tuples in relational databases. While there is a voluminous literature on specifying and modeling fine-grained access controls, less work has been done to address the performance issues of database systems with fine-grained access controls. This thesis addresses the performance issues of fine-grained access controls and proposes corresponding solutions. In particular, the following issues are addressed: effective storage of massive access controls, efficient query planning for secure query evaluation, and accurate cardinality estimation for access controlled data. Because fine-grained access controls specify access rights from each user to each piece of data in the system, they are effectively a massive matrix of the size as the product of the number of users and the size of data. Therefore, fine-grained access controls require a very compact encoding to be feasible. The proposed storage system in this thesis achieves an unprecedented level of compactness by leveraging the high correlation of access controls found in real system data. This correlation comes from two sides: the structural similarity of access rights between data, and the similarity of access patterns from different users. This encoding can be embedded into a linearized representation of XML data such that a query evaluation framework is able to compute the answer to the access controlled query with minimal disk I/O to the access controls. Query optimization is a crucial component for database systems. This thesis proposes an intelligent query plan caching mechanism that has lower amortized cost for query planning in the presence of fine-grained access controls. The rationale behind this query plan caching mechanism is that the queries, customized by different access controls from different users, may share common upper-level join trees in their optimal query plans. Since join plan generation is an expensive step in query optimization, reusing the upper-level join trees will reduce query optimization significantly. The proposed caching mechanism is able to match efficient query plans to access controlled query plans with minimal runtime cost. In case of a query plan cache miss, the optimizer needs to optimize an access controlled query from scratch. This depends on accurate cardinality estimation on the size of the intermediate query results. This thesis proposes a novel sampling scheme that has better accuracy than traditional cardinality estimation techniques
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