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Equivalence class

About: Equivalence class is a research topic. Over the lifetime, 1257 publications have been published within this topic receiving 25821 citations.


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Proceedings ArticleDOI
15 Apr 2007
TL;DR: T-closeness as mentioned in this paper requires that the distribution of a sensitive attribute in any equivalence class is close to the distributions of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t).
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

3,281 citations

Journal ArticleDOI
TL;DR: An edit-distance algorithm for shock graphs that finds the optimal deformation path in polynomial time is employed and gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations.
Abstract: This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very high-dimensional, three steps are taken to make the search practical: 1) define an equivalence class for shapes based on shock-graph topology, 2) define an equivalence class for deformation paths based on shock-graph transitions, and 3) avoid complexity-increasing deformation paths by moving toward shock-graph degeneracy. Despite these steps, which tremendously reduce the search requirement, there still remain numerous deformation paths to consider. To that end, we employ an edit-distance algorithm for shock graphs that finds the optimal deformation path in polynomial time. The proposed approach gives intuitive correspondences for a variety of shapes and is robust in the presence of a wide range of visual transformations. The recognition rates on two distinct databases of 99 and 216 shapes each indicate highly successful within category matches (100 percent in top three matches), which render the framework potentially usable in a range of shape-based recognition applications.

773 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging, and show that more sophisticated search algorithms are likely to benefit much more.
Abstract: Two Bayesian-network structures are said to be equivalent if the set of distributions that can be represented with one of those structures is identical to the set of distributions that can be represented with the other. Many scoring criteria that are used to learn Bayesian-network structures from data are score equivalent; that is, these criteria do not distinguish among networks that are equivalent. In this paper, we consider using a score equivalent criterion in conjunction with a heuristic search algorithm to perform model selection or model averaging. We argue that it is often appropriate to search among equivalence classes of network structures as opposed to the more common approach of searching among individual Bayesian-network structures. We describe a convenient graphical representation for an equivalence class of structures, and introduce a set of operators that can be applied to that representation by a search algorithm to move among equivalence classes. We show that our equivalence-class operators can be scored locally, and thus share the computational efficiency of traditional operators defined for individual structures. We show experimentally that a greedy model-selection algorithm using our representation yields slightly higher-scoring structures than the traditional approach without any additional time overhead, and we argue that more sophisticated search algorithms are likely to benefit much more.

711 citations

Journal ArticleDOI
TL;DR: In this article, the authors show how the classification of topological phases in insulators and superconductors is changed by interactions, in the case of one-dimensional systems, focusing on the time-reversal-invariant Majorana chain (BDI symmetry class).
Abstract: In this paper we show how the classification of topological phases in insulators and superconductors is changed by interactions, in the case of one-dimensional systems. We focus on the time-reversal-invariant Majorana chain (BDI symmetry class).While the band classification yields an integer topological index k, it is known that phases characterized by values of k in the same equivalence class modulo 8 can be adiabatically transformed one to another by adding suitable interaction terms. Here we show that the eight equivalence classes are distinct and exhaustive, and provide a physical interpretation for the interacting invariant modulo 8. The different phases realize different Altland-Zirnbauer classes of the reduced density matrix for an entanglement bipartition into two half chains. We generalize these results to the classification of all one-dimensional gapped phases of fermionic systems with possible antiunitary symmetries, utilizing the algebraic framework of central extensions. We use matrix product state methods to prove our results.

668 citations

Proceedings Article
Robin Milner1
01 Sep 1971
TL;DR: A technique is given and illustrated for proving simulation and equivalence of programs; there is an analogy with Floyd''s technique for proving correctness of programs.
Abstract: A simulation relation between programs is defined which is quasi-ordering. Mutual simulation is then an equivalence relation, and by dividing out by it we abstract from a program such details as how the sequencing is controlled and how data is represented. The equivalence classes are approxiamtions to the algorithms which are realized, or expressed, by their member programs. A technique is given and illustrated for proving simulation and equivalence of programs; there is an analogy with Floyd''s technique for proving correctness of programs. Finally, necessary and sufficient conditions for simulation are given.

616 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202139
202041
201950
201837
201746
201647