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.

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.

There has been significant interest in the development of anonymization schemes for publishing graph data. However, due to a strong correlation among users’ social identities, privacy is a major concern in dealing with graph data. In this paper, we propose a privacy-preserving mechanism for publishing graph data to prevent identity disclosure. The framework is a combination of access control and privacy protection mechanisms. The access control policies define privilege set (i.e., authorized data subset) available to role/queries and their associated imprecision bounds. Only authorized role/query predicates on sensitive data are allowed by the access control mechanism. For this framework, we define the problem of k -anonymous Bi-constraint Graph Partitioning ( k -BGP) and provide its hardness results. We present heuristics for graph data partitioning to satisfy the role imprecision bounds and global information loss constraints for k -BGP problem. The privacy protection mechanism anonymizes the graph data with minimal information loss while simultaneously meeting the requirement in terms of satisfying the role bounds. This approach provides an anonymous view based on the target class of role-based workloads for graph data. We present detailed performance evaluations to demonstrate the effectiveness of our algorithms w.r.t. both meeting the role imprecision bounds and minimizing the global information loss.

Graphs are used for representing and understanding objects and their relationships for numerous applications such as social networks, semantic webs, and biological networks. Integrity assurance of data and query results for graph databases is an essential security requirement. In this paper, we propose two efficient integrity verification schemes – HMACs for graphs ( gHMAC ) for two-party data sharing, and redactable HMACs for graphs ( rgHMAC ) for third-party data sharing, such as a cloud-based graph database service. We compute one HMAC value for both the schemes and two other verification objects for rgHMAC scheme that are shared with the verifier. The proposed schemes are shown to satisfy the security requirement in terms of structural/attribute tampering to the graphs. The proposed schemes have linear complexity in terms of the number of vertices and edges in the graphs, which is shown to be optimal. Our experiments show that HMAC-based schemes are efficient as compared to the digital signature-based schemes for same size of graphs. Moreover, computing HMACs for graphs as large as 8 million vertices and edges takes as little as 55 milliseconds

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.

Social interactions play an important role in spread of a disease. In this thesis we propose a probabilistic approach to synthesize an agent-based heterogeneous population interaction model to study the spatio-temporal dynamics of an air-born epidemic, such as influenza, in a metropolitan area. The proposed methodology is generic in nature and can generate a baseline population for the cities for which detailed population summary tables are not available. The joint probabilities of population demographics are estimated using the International Public Use Microsimulation Data (IPUMS) sample data set. Based on the population density and the socio-economic status, the population is divided into three types of residential areas. Agents, representing individuals, are assigned various activities based on their education, age, and gender. Since transportation can also influence the spread of a disease, this activity, with a finite time span, is also assigned to individuals. The proposed approach is used for the city of Lahore, Pakistan. The agent-based model for Lahore is synthesized and a rule based disease spread model of influenza is simulated for the city population. The simulation results are visualized to analyze the spatio-temporal dynamics of an influenza epidemic for Lahore.

The proliferation of multimedia big data for dissemination and sharing of massive amounts of information raises important security and privacy concerns. One such concern is the composition and enforcement of privacy policies in order to securely manage access of multimedia big data. Several researchers have pointed out that for proper enforcement of privacy policies, the privacy requirements should be captured in access control systems. In this paper, we propose a hybrid approach where privacy requirements are captured in an access control system and present a framework for composition and enforcement of privacy policies. The focus is to allow a user, not a system or security administrator to compose conflict free policies for their online multimedia data. An additional requirement is that such a policy be context-aware. We also present a methodology for verifying the privacy policy in order to ensure correctness and logical consistency. The verification process is also used to ensure that sensitive security requirements are not violated when privacy rules are enforced. A prototype, named Intelligent Privacy Manager (iPM), has been implemented for sharing of multimedia big data in a secure and private manner.