There has been significant interest in the development of anonymization schemes for publishing graph data. However, privacy is a major concern in dealing with graph data. In this paper, an integrated framework for ensuring privacy in the presence of an authorization mechanism is proposed. Access control mechanisms provide additional safeguard against data breaches and ensure that only authorized information is available to end-users based on their assigned roles. The integrated framework highlights a tradeoff between privacy and authorized privileges. To attain a pre-specified privacy level, access privileges might need to be relaxed. For the proposed framework, we formulate the k-anonymous Bi-objective Graph Partitioning (k-BGP) problem and provide its hardness results. Heuristics solutions are developed to solve the constraint problem. The framework provides an anonymous view based on the target class of role-based workloads for graph data. The proposed heuristics are empirically evaluated and a detailed security analysis of the framework in terms of risk associated with re-identification attack is conducted.

The emergence of cloud computing infrastructure and Semantic Web technologies has created unprecedented opportunities for composing large-scale business processes and workflow-based applications that span multiple organizational domains. A key challenge related to composition of such multi-organizational business processes and workflows is posed by the security and access control policies of the underlying organizational domains. In this paper, we propose a framework for verifying secure composability of distributed workflows in an autonomous multi-domain environment. The objective of workflow composability verification is to ensure that all the users or processes executing the designated workflow tasks conform to the time-dependent security policy specifications of all collaborating domains. A key aspect of such verification is to determine the time-dependent schedulability of distributed workflows, assumed to be invoked on a recurrent basis. We use a two-step approach for verifying secure workflow composability. In the first step, a distributed workflow is decomposed into domain-specific projected workflows and is verified for conformance with the respective domain's security and access control policy. In the second step, the cross-domain dependencies amongst the workflow tasks performed by different collaborating domains are verified.

Access control mechanisms and Privacy Protection Mechanisms (PPM) have been proposed for data streams. The access control for a data stream allows roles access to tuples satisfying an authorized predicate sliding-window query. Sharing the sensitive stream data without PPM can compromise the privacy. The PPM meets privacy requirements, e.g., k-anonymity or l-diversity by generalization of stream data. Imprecision introduced by generalization can be reduced by delaying the publishing of stream data. However, the delay in sharing the stream tuples to achieve better accuracy can lead to false-negatives if the tuples are held by PPM while the query predicate is evaluated. Administrator of an access control policy defines the imprecision bound for each query. The challenge for PPM is to optimize the delay in publishing of stream data so that the imprecision bound for the maximum number of queries is satisfied. We formulate the precision-bounded access control for privacy-preserving data streams problem, present the hardness results, provide an anonymization algorithm, and conduct experimental evaluation of the proposed algorithm. Experiments demonstrate that the proposed heuristic provides better precision for a given data stream access control policy as compared to the minimum or maximum delay heuristics proposed in existing literature.

Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized user can still compromise the privacy of a person leading to identity disclosure. A PPM can use suppression and generalization of relational data to anonymize and satisfy privacy requirements, e.g., k-anonymity and l-diversity, against identity and attribute disclosure. However, privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the k-anonymity or l-diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art.