2426986
Quantifying Privacy Leakage through Answering Database Queries
We assume a database consists of records of individuals with private or sensitive fields. Queries on the distribution of a sensitive field within a selected population in the database can be submitted to the data center. The answers to the queries leak private information of individuals though no identification information is provided. Inspired by decision theory, we present a quantitative model for the privacy protection problem in such a database query or linkage environment in this paper. In the model, the value of information is estimated from the viewpoint of the querier.To estimate the value, we define the information state of the data user by a class of probability distributions on the set of possible confidential values. We further define the usefulness of information based on how easy the data user can locate individuals that fit the description given in the queries. These states and the usefulness of information can be modified and refined by the user's knowledge acquisition actions. The value of information is then defined as the expected gain of the privacy receiver and the privacy is protected by imposing costs on the answers of the queries for balancing the gain.
17157033
On The Damage and Compensation of Privacy Leakage
A query on the distribution of a sensitive field within a selected population in a database can be submitted to the data center, and the answer to this query can leak private information, even though no identification information is provided. Inspired by decision theory, we present a quantitative model of the privacy protection problem in such a database query environment. In our model, the user information states are defined as classes of probability distributions on the set of possible confidential values. These states can be modified and refined by knowledge acquisition actions. The data confidentiality is guaranteed by ensuring that misusing private information is more costly than any possible gain.
16388639
Cardinality-based inference control in OLAP systems: an information theoretic approach
We address the inference control problem in data cubes with some data known to users through external knowledge. The goal of inference controls is to prevent exact values of sensitive data from being inferred through answers to online analytical processing (OLAP) queries. We present an information theoretic approach for cardinality-based inference control, which simply counts the number of cells that all queries have covered thus far to determine whether a new query should be answered. Compared to previous approaches in sum-only data cubes, our new approach has a more general framework (applies to MIN, MAX and SUM) and is more effective.
7478203
Parallel routing in hypercube networks with faulty nodes
The concept of strong fault-tolerance was introduced to characterize the property of parallel routing. A network G of degree d is said to be strongly fault-tolerant if with at most d-2 faulty nodes, any two nodes u and v in G are connected by min{deg/sub f/(u), deg/sub f/(v)} node-disjoint paths, where deg/sub f/ (u) and deg/sub f/ (v) are the numbers of non-faulty neighbors of the nodes u and v in G, respectively. We show that the hypercube networks are strongly fault-tolerant and develop an algorithm that constructs the maximum number of node-disjoint paths in a hypercube network with faults. Our algorithm is optimal in terms of time and length of node-disjoint paths.
30918175
The Difference Between one Tape and two Tapes: with Respect to Reversal Complexity
Abstract Reversal complexity on 1-tape and 2-tape Turing machine models discussed. We show that with respect to reversal complexity there is an intrinsic difference between 1-tape and 2-tape Turing machines. More precisely, we show that in the deterministic case, 2-tape Turing machines can simulate k-tape Turing machines without much increase in reversals while 1-tape Turing machines do not have such a property if P ≠ PSPACE; in the nondeterministic case, reversal complexity is “too” powerful to be a complexity measure on 2-tape Turing machines, but on 1-tape Turing machines it is a reasonable complexity measure which is linearly related to the space complexity.
206057505
Algorithmic Graph Embeddings
The complexity of embedding a graph into a variety of topological surfaces is investigated. A new data structure for graph embeddings is introduced and shown to be superior to the previously known data structures. In particular, the new data structure efficiently supports all on-line operations for general graph embeddings. Based on this new data structure, very efficient algorithms are developed to solve the problem “given a graph G and an integer k, construct a genus k embedding for the graph G” for a large range of the integers k and for a large class of graphs.
784309
Learning subjective relevance to facilitate information access
As the amount of available electronic information is dramatically increasing, the ability for rapid and e ective access to information has become critical. Most traditional information access methods rely on measures of relevance based on information content. We propose a new approach which augments existing information access methods with subjective relevance learned from user feedback. We developed an adaptive system which helps users access information by employing learned knowledge about which documents are likely to be relevant, given the current user's information need and user pro le. This system is based on a model, called a relevance network, which learns and generalizes relevance information in a rapid, cost-e ective, and incremental manner. We present the design of the relevance network and results of experimental evaluation.