자연주의적 탐구조사(Naturalistic Inquiry)

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Data Mining - Concepts and Techniques.

From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain identifying attributes.In this article, we show using two simple attacks that a k-anonymized dataset has some subtle but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and we show that k-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 criterion called e-diversity that can defend against such attacks. In addition to building a formal foundation for e-diversity, we show in an experimental evaluation that e-diversity is practical and can be implemented efficiently.

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L-diversity: Privacy beyond k-anonymity

In a hierarchical access control system, users are partitioned into a number of classes—called security classes—which are organized in a hierarchy. Hierarchies arise in systems where some users have higher privileges than others and a security class inherits the privileges of its descendant classes. The problem of key assignment in such systems is how to assign cryptographic keys to users and resources to properly enforce access rights. Its crucial goal is efficiency: the number of keys a user obtains, computation a user performs, and amount of resources the server is required to maintain should be minimized. 
In this work, we present a fully-dynamic and very efficient solution to the key assignment problem that is also provably secure for a strong notion of security. We then show how the model can be extended to time-based policies where users obtain access rights only for a specific duration of time, and subsequently present our time-based key assignment solution. Finally, we explain how similar techniques can be used to efficiently enforce access control policies in geo-spatial systems and describe our construction for such systems as well.

Key management in hierarchical access control systems

Secure two-party input-size reduction: Challenges, solutions and applications

This article presents a new algorithm for finding oligonucleotide signatures that are specific and sensitive for organisms or groups of organisms in large-scale sequence datasets. We assume that the organisms have been organized in a hierarchy, for example, a phylogenetic tree. The resulting signatures, binding sites for primers and probes, match the maximum possible number of organisms in the target group while having at most k matches outside of the target group.The key step in the algorithm is the use of the lowest common ancestor (LCA) to search the organism hierarchy; this allows the combinatorial problem in almost linear time (empirically observed) to be solved. The presented algorithm improves performance by several orders of magnitude in terms of both memory consumption and runtime when compared to the best-known previous algorithms while giving identical, exact solutions.This article gives a formal description of the algorithm, discusses details of our concrete, publicly available implementation, and presents the results from our performance evaluation.

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Efficient relaxed search in hierarchically clustered sequence datasets

We give a randomized algorithm for estimating the score vector of matches between a text string of length~$N$ and a pattern string of length~$M$; this is the vector obtained when the pattern is slid along the text, and the number of matches is counted for each position. The randomized algorithm takes deterministic time $O( (N / M ) {\textstyle {\it Conv}} (M) )$ where ${\textstyle {\it Conv}} (M)$ is the time for performing a convolution of two vectors of size $M$ each. The algorithm finds an unbiased estimator of the scores, whose variance is particularly small for scores that are close to $M$, i.e., for approximate occurrences of the pattern in the text. No assumptions are made about the probabilistic characteristics of the input, or about the number of different symbols appearing in $T$ or $P$ (i.e., the alphabet size need not be much smaller than $M$). The solution extends to the weighted case and to higher dimensions.

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An Algorithm for Estimating All Matches Between Two Strings

Assume we are given ann ×n binary image containing horizontally convex features; i.e., for each feature, each of its row's pixels form an interval on that row. In this paper we consider the problem of assigning topological numbers to such features, i.e., assign a number to every featuref so that all features to the left off in the image have a smaller number assigned to them. This problem arises in solutions to the stereo matching problem. We present a parallel algorithm to solve the topological numbering problem inO(n) time on ann ×n mesh of processors. The key idea of our solution is to create a tree from which the topological numbers can be obtained even though the tree does not uniquely represent the "to the left of" relationship of the features.

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Mikhail J. Atallah

Papers

Key management in hierarchical access control systems

Secure two-party input-size reduction: Challenges, solutions and applications

Efficient relaxed search in hierarchically clustered sequence datasets

An Algorithm for Estimating All Matches Between Two Strings

Topological numbering of features on a mesh