The Experts below are selected from a list of 8541 Experts worldwide ranked by ideXlab platform
Bhavani Thuraisingham - One of the best experts on this subject based on the ideXlab platform.
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Scrub-tcpdump: A multi-level packet anonymizer demonstrating privacy/analysis tradeoffs
2016Co-Authors: William Yurcik, Clay Woolam, Greg Hellings, Latifur Khan, Bhavani ThuraisinghamAbstract:Abstract—To promote sharing of packet traces across secu-rity domains we introduce SCRUB-tcpdump, a tool that adds multi-field multi-option Anonymization to tcpdump functionality. Experimental results show how SCRUB-tcpdump provides flexi-bility to balance the often conflicting requirements for privacy protection versus security analysis. Specifically, we demonstrate with empirical experimentation how different SCRUB-tcpdump Anonymization options applied to the same data set can result in different levels of privacy protection and security analysis. Based on these results we propose that optimal network data sharing needs to have different levels of Anonymization tailored to the participating organizations in order to tradeoff the risks of potential loss or disclosure of sensitive information. Index Terms—network data sharing, security data sharing, privacy protection, Anonymization, data obfuscation, network monitoring, network intrusion detection, network packet traces I
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scrub tcpdump a multi level packet anonymizer demonstrating privacy analysis tradeoffs
International Workshop on Security, 2007Co-Authors: William Yurcik, Clay Woolam, Greg Hellings, Latifur Khan, Bhavani ThuraisinghamAbstract:To promote sharing of packet traces across security domains we introduce SCRUB-tcpdump, a tool that adds multi-field multi-option Anonymization to tcpdump functionality. Experimental results show how SCRUB-tcpdump provides flexibility to balance the often conflicting requirements for privacy protection versus security analysis. Specifically, we demonstrate with empirical experimentation how different SCRUB-tcpdump Anonymization options applied to the same data set can result in different levels of privacy protection and security analysis. Based on these results we propose that optimal network data sharing needs to have different levels of Anonymization tailored to the participating organizations in order to tradeoff the risks of potential loss or disclosure of sensitive information.
William Yurcik - One of the best experts on this subject based on the ideXlab platform.
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Scrub-tcpdump: A multi-level packet anonymizer demonstrating privacy/analysis tradeoffs
2016Co-Authors: William Yurcik, Clay Woolam, Greg Hellings, Latifur Khan, Bhavani ThuraisinghamAbstract:Abstract—To promote sharing of packet traces across secu-rity domains we introduce SCRUB-tcpdump, a tool that adds multi-field multi-option Anonymization to tcpdump functionality. Experimental results show how SCRUB-tcpdump provides flexi-bility to balance the often conflicting requirements for privacy protection versus security analysis. Specifically, we demonstrate with empirical experimentation how different SCRUB-tcpdump Anonymization options applied to the same data set can result in different levels of privacy protection and security analysis. Based on these results we propose that optimal network data sharing needs to have different levels of Anonymization tailored to the participating organizations in order to tradeoff the risks of potential loss or disclosure of sensitive information. Index Terms—network data sharing, security data sharing, privacy protection, Anonymization, data obfuscation, network monitoring, network intrusion detection, network packet traces I
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scrub tcpdump a multi level packet anonymizer demonstrating privacy analysis tradeoffs
International Workshop on Security, 2007Co-Authors: William Yurcik, Clay Woolam, Greg Hellings, Latifur Khan, Bhavani ThuraisinghamAbstract:To promote sharing of packet traces across security domains we introduce SCRUB-tcpdump, a tool that adds multi-field multi-option Anonymization to tcpdump functionality. Experimental results show how SCRUB-tcpdump provides flexibility to balance the often conflicting requirements for privacy protection versus security analysis. Specifically, we demonstrate with empirical experimentation how different SCRUB-tcpdump Anonymization options applied to the same data set can result in different levels of privacy protection and security analysis. Based on these results we propose that optimal network data sharing needs to have different levels of Anonymization tailored to the participating organizations in order to tradeoff the risks of potential loss or disclosure of sensitive information.
Fabian Prasser - One of the best experts on this subject based on the ideXlab platform.
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Design and evaluation of a data Anonymization pipeline to promote Open Science on COVID-19
Scientific Data, 2020Co-Authors: Carolin E. M. Jakob, Florian Kohlmayer, Thierry Meurers, Jörg Janne Vehreschild, Fabian PrasserAbstract:The Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) is a European registry for studying the epidemiology and clinical course of COVID-19. To support evidence-generation at the rapid pace required in a pandemic, LEOSS follows an Open Science approach, making data available to the public in real-time. To protect patient privacy, quantitative Anonymization procedures are used to protect the continuously published data stream consisting of 16 variables on the course and therapy of COVID-19 from singling out, inference and linkage attacks. We investigated the bias introduced by this process and found that it has very little impact on the quality of output data. Current laws do not specify requirements for the application of formal Anonymization methods, there is a lack of guidelines with clear recommendations and few real-world applications of quantitative Anonymization procedures have been described in the literature. We therefore believe that our work can help others with developing urgently needed Anonymization pipelines for their projects.
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efficient protection of health data from sensitive attribute disclosure
Medical Informatics Europe, 2020Co-Authors: Raffael Bild, Johanna Eicher, Fabian PrasserAbstract:Biomedical research has become data-driven. To create the required big datasets, health data needs to be shared or reused out of the context of its initial purpose. This leads to significant privacy challenges. Data Anonymization is an important protection method where data is transformed such that privacy guarantees can be provided according to formal models. For applications in practice, Anonymization methods need to be integrated into scalable and robust tools. In this work, we focus on the problem of scalability. Protecting biomedical data from inference attacks is challenging, in particular for numeric data. An important privacy model in this context is t-closeness, which has also been defined for attribute values which are totally ordered. However, directly implementing a scalable algorithmic representation of the mathematical definition of the model proves difficult. In this paper we therefore present a series of optimizations that can be used to achieve efficiency in production use. An experimental evaluation shows that our approach reduces execution times of Anonymization processes involving t-closeness by up to a factor of two.
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putting statistical disclosure control into practice the arx data Anonymization tool
Medical Data Privacy Handbook, 2015Co-Authors: Fabian Prasser, Florian KohlmayerAbstract:The sharing of sensitive personal data has become a core element of biomedical research. To protect privacy, a broad spectrum of techniques must be implemented, including data Anonymization. In this article, we present ARX, an Anonymization tool for structured data which supports a broad spectrum of methods for statistical disclosure control by providing (1) models for analyzing re-identification risks, (2) risk-based Anonymization, (3) syntactic privacy criteria, such as k-anonymity, l-diversity, t-closeness and δ-presence, (4) methods for automated and manual evaluation of data utility, and (5) an intuitive coding model using generalization, suppression and microaggregation. ARX is highly scalable and allows for anonymizing datasets with several millions of records on commodity hardware. Moreover, it offers a comprehensive graphical user interface with wizards and visualizations that guide users through different aspects of the Anonymization process. ARX is not just a toolbox, but a fully-fledged application, meaning that all implemented methods have been harmonized and integrated with each other. It is well understood that balancing privacy and data utility requires user feedback. To facilitate this interaction, ARX is highly configurable and provides various methods for exploring the solution space.
El A Abbadi - One of the best experts on this subject based on the ideXlab platform.
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anonimos an lp based approach for anonymizing weighted social network graphs
IEEE Transactions on Knowledge and Data Engineering, 2012Co-Authors: Sudipto Das, Omer Egecioglu, El A AbbadiAbstract:The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity Anonymization and structural Anonymization. But with the growing interest in analyzing social networks as a weighted network, edge weight Anonymization is also gaining importance. We present Anonimos, a Linear Programming-based technique for Anonymization of edge weights that preserves linear properties of graphs. Such properties form the foundation of many important graph-theoretic algorithms such as shortest paths problem, k-nearest neighbors, minimum cost spanning tree, and maximizing information spread. As a proof of concept, we apply Anonimos to the shortest paths problem and its extensions, prove the correctness, analyze complexity, and experimentally evaluate it using real social network data sets. Our experiments demonstrate that Anonimos anonymizes the weights, improves k-anonymity of the weights, and also scrambles the relative ordering of the edges sorted by weights, thereby providing robust and effective Anonymization of the sensitive edge-weights. We also demonstrate the composability of different models generated using Anonimos, a property that allows a single anonymized graph to preserve multiple linear properties.
Elisa Bertino - One of the best experts on this subject based on the ideXlab platform.
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A supermodularity-based differential privacy preserving algorithm for data Anonymization
IEEE Transactions on Knowledge and Data Engineering, 2014Co-Authors: Mohamed R. Fouad, Khaled Elbassioni, Elisa BertinoAbstract:Maximizing data usage and minimizing privacy risk are two conflicting\ngoals. Organizations always apply a set of transformations on their data\nbefore releasing it. While determining the best set of transformations\nhas been the focus of extensive work in the database community, most of\nthis work suffered from one or both of the following major problems:\nscalability and privacy guarantee. Differential Privacy provides a\ntheoretical formulation for privacy that ensures that the system\nessentially behaves the same way regardless of whether any individual is\nincluded in the database. In this paper, we address both scalability and\nprivacy risk of data Anonymization. We propose a scalable algorithm that\nmeets differential privacy when applying a specific random sampling. The\ncontribution of the paper is two-fold: 1) we propose a personalized\nAnonymization technique based on an aggregate formulation and prove that\nit can be implemented in polynomial time; and 2) we show that combining\nthe proposed aggregate formulation with specific sampling gives an\nAnonymization algorithm that satisfies differential privacy. Our results\nrely heavily on exploring the supermodularity properties of the risk\nfunction, which allow us to employ techniques from convex optimization.\nThrough experimental studies we compare our proposed algorithm with\nother Anonymization schemes in terms of both time and privacy risk.