Attack vector analysis and privacy-preserving social network data publishing

This paper addresses the problem of privacy-preserving data publishing for social network. Research on protecting the privacy of individuals and the confidentiality of data in social network has recently been receiving increasing attention. Privacy is an important issue when one wants to make use of data that involves individuals' sensitive information, especially in a time when data collection is becoming easier and sophisticated data mining techniques are becoming more efficient. In this paper, we discuss various privacy attack vectors on social networks. We present algorithms that sanitize data to make it safe for release while preserving useful information, and discuss ways of analyzing the sanitized data. This study provides a summary of the current state-of-the-art, based on which we expect to see advances in social networks data publishing for years to come.

Neighbourhood-pair attack in social network data publishing

Vertex re-identification is one of the significant and challenging problems in social network. In this paper, we show a new type of vertex reidentification attack called neighbourhood-pair attack. This attack utilizes the neighbourhood topologies of two connected vertices. We show both theoretically and empirically that this attack is possible on anonymized social network and has higher re-identification rate than the existing structural attacks.

PPFSCADA: Privacy preserving framework for SCADA data publishing

Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, such as electricity, gas, water, waste, railway, and traffic. Recent attacks on SCADA systems highlight the need for stronger SCADA security. Thus, sharing SCADA traffic data has become a vital requirement in SCADA systems to analyze security risks and develop appropriate security solutions. However, inappropriate sharing and usage of SCADA data could threaten the privacy of companies and prevent sharing of data. In this paper, we present a privacy preserving strategy-based permutation technique called PPFSCADA framework, in which data privacy, statistical properties and data mining utilities can be controlled at the same time. In particular, our proposed approach involves: (i) vertically partitioning the original data set to improve the performance of perturbation; (ii) developing a framework to deal with various types of network traffic data including numerical, categorical and hierarchical attributes; (iii) grouping the portioned sets into a number of clusters based on the proposed framework; and (iv) the perturbation process is accomplished by the alteration of the original attribute value by a new value (clusters centroid). The effectiveness of the proposed PPFSCADA framework is shown through several experiments on simulated SCADA, intrusion detection and network traffic data sets. Through experimental analysis, we show that PPFSCADA effectively deals with multivariate traffic attributes, producing compatible results as the original data, and also substantially improving the performance of the five supervised approaches and provides high level of privacy protection. © 2014 Published by Elsevier B.V. All rights reserved.

Privacy-preserving mechanism for social network data publishing

 Privacy is receiving growing concern from various parties especially consumers due to the simplification of the collection and distribution of personal data. This research focuses on preserving privacy in social network data publishing. The study explores the data anonymization mechanism in order to improve privacy protection of social network users. We identified new type of privacy breach and has proposed an effective mechanism for privacy protection.

Privacy in data publishing for tailored recommendation scenarios

Personal information is increasingly gathered and used for providing services tailored to user preferences, but the datasets used to provide such functionality can represent serious privacy threats if not appropriately protected. Work in privacy-preserving data publishing targeted privacy guarantees that protect against record re-identification, by making records indistinguishable, or sensitive attribute value disclosure, by introducing diversity or noise in the sensitive values. However, most approaches fail in the high-dimensional case, and the ones that don’t introduce a utility cost incompatible with tailored recommendation scenarios. This paper aims at a sensible trade-off between privacy and the benefits of tailored recommendations, in the context of privacy-preserving data publishing. We empirically demonstrate that significant privacy improvements can be achieved at a utility cost compatible with tailored recommendation scenarios, using a simple partition-based sanitization method.

Extensible software for research data capture

Projects funded by the Australian National Data Service(ANDS). The specific projects that were funded included: a) Greenhouse Gas Emissions Project (N2O) with Prof. Peter Grace from QUT’s Institute of Sustainable Resources. b) Q150 Project for the management of multimedia data collected at Festival events with Prof. Phil Graham from QUT’s Institute of Creative Industries. c) Bio-diversity environmental sensing with Prof. Paul Roe from the QUT Microsoft eResearch Centre. For the purposes of these projects the Eclipse Rich Client Platform (Eclipse RCP) was chosen as an appropriate software development framework within which to develop the respective software. This poster will present a brief overview of the requirements of the projects, an overview of the experiences of the project team in using Eclipse RCP, report on the advantages and disadvantages of using Eclipse and it’s perspective on Eclipse as an integrated tool for supporting future data management requirements.

Developing Linked Data Applications

Publishing Linked Data SPARQL Graph Store Protocol Linked Data Platform Reflection on Data Publishing

Privacy threat analysis of social network data

Social network data has been increasingly made publicly available and analyzed in a wide spectrum of application domains. The practice of publishing social network data has brought privacy concerns to the front. Serious concerns on privacy protection in social networks have been raised in recent years. Realization of the promise of social networks data requires addressing these concerns. This paper considers the privacy disclosure in social network data publishing. In this paper, we present a systematic analysis of the various risks to privacy in publishing of social network data. We identify various attacks that can be used to reveal private information from social network data. This information is useful for developing practical countermeasures against the privacy attacks.

Privacy preserving social network data publication

The introduction of online social networks (OSN) has transformed the way people connect and interact with each other as well as share information. OSN have led to a tremendous explosion of network-centric data that could be harvested for better understanding of interesting phenomena such as sociological and behavioural aspects of individuals or groups. As a result, online social network service operators are compelled to publish the social network data for use by third party consumers such as researchers and advertisers. As social network data publication is vulnerable to a wide variety of reidentification and disclosure attacks, developing privacy preserving mechanisms are an active research area. This paper presents a comprehensive survey of the recent developments in social networks data publishing privacy risks, attacks, and privacy-preserving techniques. We survey and present various types of privacy attacks and information exploited by adversaries to perpetrate privacy attacks on anonymized social network data. We present an in-depth survey of the state-of-the-art privacy preserving techniques for social network data publishing, metrics for quantifying the anonymity level provided, and information loss as well as challenges and new research directions. The survey helps readers understand the threats, various privacy preserving mechanisms, and their vulnerabilities to privacy breach attacks in social network data publishing as well as observe common themes and future directions.

Workshop on marine mammal research & monitoring in the National Marine Sanctuaries, Wailea, Maui, Hawaii, 28 November 1999

The Second National Workshop on Marine Mammal Research and Monitoring in the National Marine Sanctuaries was held on 28 November 1999 in Maui, Hawaii. The workshop preceded the Thirteenth Biennial Conference on the Biology of Marine Mammals, and provided an opportunity to review and promote marine mammal research and monitoring in the National Marine Sanctuaries (NMS). The purpose of the workshop was to bring together researchers and sanctuary staff and to improve marine mammal research and monitoring throughout the sanctuaries. Discussion topics included: potential multi-sanctuary projects, sources of funding for multi-sanctuary projects, services and equipment for researchers through the sanctuaries, consolidating small levels of funding, help in funding and support for writing up data, publishing documents in Technical Memoranda, and letters of support. Representatives from the NMS national office and nine sanctuaries provided participants with overviews of marine mammal research within the sanctuaries. Presentations were also given by representatives from the National Marine Fisheries Service’s Permits and Health and Stranding programs. During the breakout working groups, there were several comments and suggestions consistent among each of the groups to improve marine mammal research. Each group emphasized the need to improve communication among researchers and to better share data. These suggestions included web-based information networks, advisory panels, and workshops. Regionally based research projects were also emphasized. In order to best study marine mammal populations, collaborative studies must take place throughout multiple sanctuaries. In order to achieve these large scale studies, funding and staffing must be directed towards these studies and distributed among each of the sanctuaries so that they may all be able to have the staffing, equipment, and vessels necessary to achieve a collaborative, ecosystem-based, regional marine mammal monitoring program. It will take several years to achieve all of the suggestions from the workshop, but thanks to the workshop participants, the National Marine Sanctuary Program has begun to direct marine mammal research and monitoring in order to achieve the goals of the workshop. This document provides a summary of the workshop with a focus on key points/main issues. We have included contact information intended to encourage continued collaboration among the individuals and organizations represented at the 1999 Marine Mammal Research and Monitoring in the National Marine Sanctuaries Workshop. (PDF contains 71 pages.)

基于模型驱动的实时数据发布系统研究与应用

流程企业生产过程反映了企业的工艺路线、资源配置及其制造能力，生产过程的稳定运行影响着产品生命周期的各个阶段，因此进行生产过程的实时监控非常必要。生产过程中的大量装置所包含的静态数据和实时的动态数据构成了生产过程监控的数据基础，因此实现生产过程的实时监控依赖于基于装置对象的生产过程的快速配置。 本文以国家高技术研究发展计划（863计划）课题“面向全流程多尺度的综合自动化集成应用平台”为背景，针对流程企业生产过程监控的具体需求，研究并开发了基于模型驱动的实时数据发布系统，实现了基于装置对象的生产过程的快速配置。论文主要工作如下： 1.给出了基于数据驱动机制的装置对象描述模型，建立了面向流程企业生产过程的装置资源库。在此基础上给出了一种基于装置对象描述模型的生产过程快速配置方法。 2.针对流程企业生产过程监控的具体需求，引入XML-QL与复合事件的时序逻辑模型，提出了一个基于XML和复合事件的发布/订阅模型，给出了基于订阅树的复合事件匹配算法。最后在生产过程模型的基础上设计了一个实时数据发布框架。 3.基于.NET平台，设计开发了基于装置对象模型驱动的实时数据发布系统，并基于某石化企业的核心数据进行了应用验证，取得了良好的效果。 关键词：生产过程监控；模型驱动；发布/订阅；复合事件

A decade of AIDS literature.

From 1983 through mid-1991, more than 200,000 MEDLINE entries were AIDS-related. Close to 60% of the journals indexed in MEDLINE published at least one article on AIDS during the past ten years. As reflected by a subset of 29,077 MEDLINE records, the literature of AIDS has grown to encompass 29 languages and 65 countries. A bibliometric study of the medical literature helps to demonstrate the progression of AIDS as a world health problem and the concomitant expansion of the research effort underway to control it.

Trends in Digital Cultural Heritage Management and Applications

We present some recent trends in the field of digital cultural heritage management and applications including digital cultural data curation, interoperability, open linked data publishing, crowd sourcing, visualization, platforms for digital cultural heritage, and applications. We present some examples from research and development projects of MUSIC/TUC in those areas.

It's always been about the links

Abstract The World Wide Web Consortium, W3C, is known for standards like HTML and CSS but there's a lot more to it than that. Mobile, automotive, publishing, graphics, TV and more. Then there are horizontal issues like privacy, security, accessibility and internationalisation. Many of these assume that there is an underlying data infrastructure to power applications. In this session, W3C's Data Activity Lead, Phil Archer, will describe the overall vision for better use of the Web as a platform for sharing data and how that translates into recent, current and possible future work. What's the difference between using the Web as a data platform and as a glorified USB stick? Why does it matter? And what makes a standard a standard anyway? Speaker Biography Phil Archer Phil Archer is Data Activity Lead at W3C, the industry standards body for the World Wide Web, coordinating W3C's work in the Semantic Web and related technologies. He is most closely involved in the Data on the Web Best Practices, Permissions and Obligations Expression and Spatial Data on the Web Working Groups. His key themes are interoperability through common terminology and URI persistence. As well as work at the W3C, his career has encompassed broadcasting, teaching, linked data publishing, copy writing, and, perhaps incongruously, countryside conservation. The common thread throughout has been a knack for communication, particularly communicating complex technical ideas to a more general audience.

Utility-aware social network graph anonymization

As the need for social network data publishing continues to increase, how to preserve the privacy of the social network data before publishing is becoming an important and challenging issue. A common approach to address this issue is through anonymization of the social network structure. The problem with altering the structure of the links relationship in social network data is how to balance between the gain of privacy and the loss of information (data utility). In this paper, we address this problem. We propose a utility-aware social network graph anonymization. The approach is based on a new metric that calculates the utility impact of social network link modification. The metric utilizes the shortest path length and the neighborhood overlap as the utility value. The value is then used as a weight factor in preserving structural integrity in the social network graph anonymization. For any modification made to the social network links, the proposed approach guarantees that the distance between vertices in the modified social network stays as close as the original social network graph prior to the modification. Experimental evaluation shows that the proposed metric improves the utility preservation as compared to the number-of-change metric.