International Conference on Data Engineering 

About: International Conference on Data Engineering is an academic conference. The conference publishes majorly in the area(s): Relational database & Query optimization. Over the lifetime, 6255 publications have been published by the conference receiving 251626 citations.

Mining sequential patterns

Abstract: We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction. >

t-Closeness: Privacy Beyond k-Anonymity and l-Diversity

Abstract: The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (ie, a set of records that are indistinguishable from each other with respect to certain "identifying" attributes) contains at least k records Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute In this paper we show that l-diversity has a number of limitations In particular, it is neither necessary nor sufficient to prevent attribute disclosure We propose a novel privacy notion called t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (ie, the distance between the two distributions should be no more than a threshold t) We choose to use the earth mover distance measure for our t-closeness requirement We discuss the rationale for t-closeness and illustrate its advantages through examples and experiments

L-diversity: privacy beyond k-anonymity

Abstract: Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called \kappa-anonymity has gained popularity. In a \kappa-anonymized dataset, each record is indistinguishable from at least k—1 other records with respect to certain "identifying" attributes. In this paper we show with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems. First, we show that an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. Second, attackers often have background knowledge, and we show that \kappa-anonymity does not guarantee privacy against attackers using background knowledge. We give a detailed analysis of these two attacks and we propose a novel and powerful privacy definition called \ell-diversity. In addition to building a formal foundation for \ell-diversity, we show in an experimental evaluation that \ell-diversity is practical and can be implemented efficiently.

The Skyline operator

Abstract: We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SSL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.

Data cube: a relational aggregation operator generalizing GROUP-BY, CROSS-TAB, and SUB-TOTALS

Abstract: Data analysis applications typically aggregate data across many dimensions looking for unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional answers. Applications need the N-dimensional generalization of these operators. The paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The cube treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensionaI cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. Aggregation points are represented by an "infinite value": ALL, so the point (ALL,ALL,...,ALL, sum(*)) represents the global sum of all items. Each ALL value actually represents the set of values contributing to that aggregation.