... In this paper we address the problem of classifying information by enforcing explicit data classification as well as inference and association constraints. We formulate the problem of determining a classification that ensures satisfaction of the constraints, while at the same time guaranteeing that information will not be overclassified. We present an approach to the solution of this problem and give an algorithm implementing it which is linear in simple cases, and quadratic in the general case. We also analyze a variant of the problem that is NP-complete.

Abstract. We introduce an innovative decomposition technique which reduces a multi–dimensional searching problem to a sequence of one–dimensional problems, each one easily manageable in optimal time×space complexity using traditional searching strategies. The reduction has no additional storage requirement and the time complexity to reconstruct the result of the original multi–dimensional query is linear in the dimension. More precisely, we show how to preprocess a set of S ⊆ IN d of multi–dimensional objects into a data structure requiring O(m log n) space, where m = |S | and n is the maximum number of different values for each coordinate. The obtained data structure is implicit, i.e. does not use pointers, and is able to answer the exact match query in 7(d − 1) steps. Additionally, the model of computation required for querying the data structure is very simple; the only arithmetic operation needed is the addition and no shift operation is used. The technique introduced, overcoming the multi–dimensional bottleneck, can be also applied to non traditional models of computation as external memory, distributed, and hierarchical environments. Additionally, we will show how the proposed technique permits the effective realizability of the well known perfect hashing techniques on real data. The algorithms for building the data structure are easy to implement and run in polynomial time. 1

Despite advances in recent years in the area of mandatory access control in database systems, today's information repositories remain vulnerable to inference and data association attacks that can result in serious information leakage. Such information leakage can be prevented by properly classifying information according to constraints that express relationships among the security levels of data objects. In this paper we address the problem of classifying information by enforcing explicit data classification as well as inference and association constraints. We formulate the problem of determining a classification that ensures satisfaction of the constraints, while at the same time guaranteeing that information will not be unnecessarily overclassified. We present an approach to the solution of this problem and give an algorithm implementing it which is linear in simple cases, and low-order polynomial (n²) in the general case. We also analyze a variation of the problem which is NP-hard.

Despite advances in recent years in the area of mandatory access control in database systems, today's information repositories remain vulnerable to inference and data association attacks that can result in serious information leakage. Without support for coping against these attacks, sensitive information can be put at risk because of release of other (less sensitive) related information. The ability to protect information diclosure against such improper leakage would be of great bene t to governmental, public, and private institutions, which are, today more than ever, required to make portions of their data available for external realease.

Despite advances in recent years in the area of mandatory access control in database systems, today’s information repositories remain vulnerable to inference and data association attacks that can result in serious information leakage. Without support for coping against these attacks, sensitive information can be put at risk because of release of other (less sensitive) related information. The ability to protect information diclosure against such improper leakage would be of great benefit to governmental, public, and private institutions, which are, today more than ever, required to make portions of their data available for external realease. In this paper we address the problem of classifying information by enforcing explicit data classification as well as inference and association constraints. We formulate the problem of determining a classification that ensures satisfaction of the constraints, while at the same time guaranteeing that information will not be overclassified. We present an approach to the solution of this problem and give an algorithm implementing it which is linear in simple cases, and quadratic in the general case. We also analyze a variant of the problem that is NP-complete.