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LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS

Description: An inference is the process of transforming unclassified data values into confidential data values. Most previous research in inference control has studied the use of statistical aggregates to deduce individual records. However, several other types of inference are also possible. Unknown functional dependencies may be apparent to users who have 'expert' knowledge about the characteristics of a population. Some correlations between attributes may be concluded from 'commonly-known' facts about the world. To counter these threats, security managers should use random sampling of databases of similar populations, as well as expert systems. 'Expert' users of the DATABASE SYSTEM may form inferences from the variable performance of the user interface. Users may observe on-line turn-around time, accounting statistics. the error message received, and the point at which an interactive protocol sequence fails. One may obtain information about the frequency distributions of attribute values, and the validity of data object names from this information. At the back-end of a database system, improved software engineering practices will reduce opportunities to bypass functional units of the database system. The term 'DATA OBJECT' should be expanded to incorporate these data object types which generate new classes of threats. The security of DATABASES and DATABASE SySTEMS must be recognized as separate but related problems. Thus, by increased awareness of lower level inferences, system security managers may effectively nullify the threat posed by lower level inferences.
Date: February 1, 1984
Creator: Lipton, D.L. & Wong, H.K.T.
Partner: UNT Libraries Government Documents Department

Predecessor and permutation existence problems for sequential dynamical systems

Description: A class of finite discrete dynamical systems, called Sequential Dynamical Systems (SDSs), was introduced in BMR99, BR991 as a formal model for analyzing simulation systems. An SDS S is a triple (G, F,n ),w here (i) G(V,E ) is an undirected graph with n nodes with each node having a state, (ii) F = (fi, fi, . . ., fn), with fi denoting a function associated with node ui E V and (iii) A is a permutation of (or total order on) the nodes in V, A configuration of an SDS is an n-vector ( b l, bz, . . ., bn), where bi is the value of the state of node vi. A single SDS transition from one configuration to another is obtained by updating the states of the nodes by evaluating the function associated with each of them in the order given by n. Here, we address the complexity of two basic problems and their generalizations for SDSs. Given an SDS S and a configuration C, the PREDECESSOR EXISTENCE (or PRE) problem is to determine whether there is a configuration C' such that S has a transition from C' to C. (If C has no predecessor, C is known as a garden of Eden configuration.) Our results provide separations between efficiently solvable and computationally intractable instances of the PRE problem. For example, we show that the PRE problem can be solved efficiently for SDSs with Boolean state values when the node functions are symmetric and the underlying graph is of bounded treewidth. In contrast, we show that allowing just one non-symmetric node function renders the problem NP-complete even when the underlying graph is a tree (which has a treewidth of 1). We also show that the PRE problem is efficiently solvable for SDSs whose state values are from ...
Date: January 1, 2002
Creator: Barrett, C. L. (Christopher L.); Hunt, H. B. (Harry B.); Marathe, M. V. (Madhav V.); Rosenkrantz, D. J. (Daniel J.) & Stearns, R. E. (Richard E.)
Partner: UNT Libraries Government Documents Department

PETSc and overture : lessons learned developing an interface between components.

Description: We consider two software packages that interact with each other as components: Overture and PETSc. An interface between these two packages could be of tremendous value to application developers in that Overture provides a simple mechanism for generating the large, sparse systems of linear equations that correspond to discretizations of a PDE, and PETSc provides a powerful collection of methods for solving these systems. Two types of interfaces are discussed: the internal interface between components, and the external interface for the application developer. We compare three basic approaches to developing the internal interface between Overture and PETSc, the final one of which is a peer-to-peer model.
Date: November 20, 2000
Creator: Buschelman, K. R.; Gropp, W. D.; McInnes, L. C. & Smith, B. F.
Partner: UNT Libraries Government Documents Department