Data Usage Control Research Articles

Query Monitoring and Analysis for Database Privacy - A Security Automata Model Approach.

Privacy and usage restriction issues are important when valuable data are exchanged or acquired by different organizations. Standard access control mechanisms either restrict or completely grant access to valuable data. On the other hand, data obfuscation limits the overall usability and may result in loss of total value. There are no standard policy enforcement mechanisms for data acquired through mutual and copyright agreements. In practice, many different types of policies can be enforced in protecting data privacy. Hence there is the need for an unified framework that encapsulates multiple suites of policies to protect the data. We present our vision of an architecture named security automata model (SAM) to enforce privacy-preserving policies and usage restrictions. SAM analyzes the input queries and their outputs to enforce various policies, liberating data owners from the burden of monitoring data access. SAM allows administrators to specify various policies and enforces them to monitor queries and control the data access. Our goal is to address the problems of data usage control and protection through privacy policies that can be defined, enforced, and integrated with the existing access control mechanisms using SAM. In this paper, we lay out the theoretical foundation of SAM, which is based on an automata named Mandatory Result Automata. We also discuss the major challenges of implementing SAM in a real-world database environment as well as ideas to meet such challenges.

Using Policy-Based Management for Privacy-Enhancing Data Access and Usage Control in Grid Environments

Preventing the misuse of personally identifiable information and preserving user privacy are key issues in the management of IT services, especially when organizational borders are crossed. However, in Grid environments only few of the recent advantages in research areas such as privacy enhancing technologies and federated identity management have been adopted so far. In this article, we first present an analysis of the differences between Grid environments and previous models of inter-organizational collaboration. Based on requirements derived thereof, we demonstrate how existing policy-based privacy management architectures can be extended to provide Grid-specific functionality and integrated into existing infrastructures. Special emphasis is put on privacy policies which can be configured by users themselves, and distinguishing between the initial data access and the later data usage control phases. We also discuss the application of this approach to a XACML-based privacy management system.

How to Prevent Internal and External Attacks on Data - Securing the Enterprise Data Flow Against Advanced Attacks

Well documented breaches have heightened the public's and regulatory agencies' concerns about how well companies are securing consumer-specific information. Despite some initial advances, sensitive information is still commonly stolen. Internal threat issues and the fact that extended partnerships lead to that, more and more tasks will be performed outside the physical boundaries of company facilities which will add another level of due diligence we must take into account. This article will present different practical methods that can help prevent advanced attacks from internal and external sources. Several of these methods go beyond the basic protection requirements for data at rest in PCI DSS 1.1 defined by the major credit card companies. Several of these solutions are applicable to booth applications, files and databases. Separation of duties is a cornerstone for true data protection. A data security policy separated from the database, file system or application environment can provide greater security across most enterprise legacy environments. This article will discuss different methods to enforce separation of duties, protection of data and controlling integrity of the security system to prevent leakage of sensitive information. Data Usage Control can complement the core protection by detecting and preventing data misuse through the direct monitoring and behavioral analysis of sensitive operations on databases and file systems. Some well documented security breaches also highlighted one area of weakness when data is in transit and, particularly, in transit within a single entity or enterprise such as on an internal network. As legislation and public concern over well-publicized security breaches pushes organizations to better secure their data, it is no longer acceptable to encrypt data only when it is stored in a database. Rather, data fields and files should be continuously encrypted as they move throughout an enterprise and beyond. Protection of the data flow can be supported by including the metadata with the protected sensitive data to provide the receiving system with required information for decryption of data. A high level of transparency can be achieved by compressing the protected data and including the metadata into the same amount of space as originally allocated. This approach can be used in most cases when protecting credit card data. The Continuously Protected Computing approach can be combined with partial encryption applied to some data fields to improve security by minimizing the need to access encryption keys and minimizing the number of platforms that require cryptographic services installed.