(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.

title: Direct interesting rule generation
abstract: An association rule generation algorithm usually generates too many rules including a lot of uninteresting ones. Many interestingness criteria are proposed to prune those uninteresting rules. However, they work in post-pruning process and hence do not improve the rule generation efficiency. We discuss properties of informative rule set and conclude that the informative rule set includes all interesting rules measured by many commonly used interestingness criteria, and that rules excluded by the informative rule set are forwardly prunable, i.e. they can be removed in the rule generation process instead of post pruning. Based on these properties, we propose a direct interesting rule generation algorithm, DIG, to directly generate interesting rules defined by any of 12 interestingness criteria. We further show experimentally that DIG is faster and uses less memory than Apriori.

title: Mining the smallest association rule set for predictions
abstract: Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this subset the informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We present an algorithm to directly generate the informative rule set, i.e., without generating all frequent itemsets first, and that accesses the database less often than other unconstrained direct methods. We show experimentally that the informative rule set is much smaller than both the association rule set and the non-redundant association rule set, and that it can be generated more efficiently.

title: Mining risk patterns in medical data
abstract: In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

title: Construct robust rule sets for classification
abstract: We study the problem of computing classification rule sets from relational databases so that accurate predictions can be made on test data with missing attribute values. Traditional classifiers perform badly when test data are not as complete as the training data because they tailor a training database too much. We introduce the concept of one rule set being more robust than another, that is, able to make more accurate predictions on test data with missing attribute values. We show that the optimal class association rule set is as robust as the complete class association rule set. We then introduce the k-optimal rule set, which provides predictions exactly the same as the optimal class association rule set on test data with up to k missing attribute values. This leads to a hierarchy of k-optimal rule sets in which decreasing size corresponds to decreasing robustness, and they all more robust than a traditional classification rule set. We introduce two methods to find k-optimal rule sets, i.e. an optimal association rule mining approach and a heuristic approximate approach. We show experimentally that a k-optimal rule set generated by the optimal association rule mining approach performs better than that by the heuristic approximate approach and both rule sets perform significantly better than a typical classification rule set (C4.5Rules) on incomplete test data.

title: Mining Optimal Class Association Rule Set
abstract: We define an optimal class association rule set to be the minimum rule set with the same prediction power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less time than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.

title: Securing distributed adaptation
abstract: Open architecture networks provide applications with fine-grained control over network elements. With this control comes the risk of misuse and new challenges to security beyond those present in conventional networks. One particular security requirement is the ability of applications to protect the secrecy and integrity of transmitted data while still allowing trusted active elements within the network to operate on that data. This paper describes mechanisms for identifying trusted nodes within a network and securely deploying adaptation instructions to those nodes while protecting application data from unauthorized access and modification. Promising experimental results of our implementation within the conductor adaptation framework are also presented, suggesting that such features can be incorporated into real networks.

title: Universal Accumulators with Efficient Nonmembership Proofs
abstract: Based on the notion of accumulators, we propose a new cryptographic scheme called universal accumulators. This scheme enables one to commit to a set of values using a short accumulator and to efficiently compute a membership witness of any value that has been accumulated. Unlike traditional accumulators, this scheme also enables one to efficiently compute a nonmembership witness of any value that has not been accumulated. We give a construction for universal accumulators and prove its security based on the strong RSA assumption. We further present a construction for dynamic universal accumulators; this construction allows one to dynamically add and delete inputs with constant computational cost. Our construction directly builds upon Camenisch and Lysyanskaya's dynamic accumulator scheme. Universal accumulators can be seen as an extension to dynamic accumulators with support of nonmembership witness. We also give an efficient zero-knowledge proof protocol for proving that a committed value is not in the accumulator. Our dynamic universal accumulator construction enables efficient membership revocation in an anonymous fashion.

title: RACCOON: a peer-based system for data integration and sharing
abstract: Recent database applications see the emerging need to support data integration in distributed, peer-to-peer environments, in which autonomous peers (sources) connected by a network are willing to exchange data and services with each other. To address related research challenges, we are developing a system called "RACCOON", which allows different sources to integrate and share their data. We use an application to show several important features of the RACCOON system. The system also suggests semantic mappings for the user to choose. We show the two different querying modes, particularly how a query is expanded using the semantic mappings in the extended querying mode to compute as many answers to the query as possible.

title: Influence and conditional influence-new interestingness measures in association rule mining
abstract: Discusses the issues of interestingness in association rule mining. First, a rule is possibly redundant or misleading even if it possesses high degrees of confidence and support. Second, association rules do not reflect the effect of negatively influential facts. Such problems are related to confidence deviation. In the paper, therefore, two new measures of interestingness, namely influence and conditional influence, are introduced to represent the effect of the antecedent on the consequent. Furthermore, the mining algorithms are extended accordingly such that certain redundant rules can be eliminated and negatively influential rules may be discovered.

title: Accelerating XML Structural Join by Partitioning
abstract: Structural join is the core part of XML queries and has a significant impact on the performance of XML queries, several classical structural join algorithms have been proposed such as Stack-tree join and XR-Tree join. In this paper, we consider to answer the problem of structural join by partitioning. We first extend the relationships between nodes to the relationships between partitions in the plane and get some observations. We then propose a new partition-based method P-Join for structural join. Based on P-Join, moreover, we present an enhanced partitioned-based spatial structural join algorithm PSSJ.