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Analysis of multilateral software confidentiality requirements.

机译:分析多边软件的机密性要求。

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摘要

Ensuring privacy and confidentiality concerns of data owners is an important aspect of a secured information system. This is particularly important for integrated systems, which allow data exchange across organizations. Governments, regulatory bodies and organizations provide legislations, regulations and guidelines for information privacy and security to ensure proper data handling. These are usually specified in natural language formats, contain default requirements and exceptions, and are often ambiguous. In addition, interacting concerns, which are often multilayered and from different stakeholders, e.g., jurisdictions, need to be considered in software development.;The evaluations conducted indicate the method and tool provide benefits, including distinguishing requirement interferences and conflicts, exception handling, and navigation between annotated documents and the goal models.;Although current limitations of the method include a manual user driven annotation step, the method provides features that assist in early analysis of confidentiality requirements from natural language sources.;Similar to other security concerns, analysis of confidentiality concerns should be integrated into the early phase of software development in order to facilitate early identification of defects---incompleteness and inconsistencies, in the requirements. This dissertation presents research conducted to develop a method to detect these defects using goal models which support defaults and exceptions. The goal models are derived from annotations of the natural language sources. A prototype tool is also developed to support the method.
机译:确保数据所有者的隐私和机密性是安全信息系统的重要方面。这对于允许跨组织交换数据的集成系统尤其重要。政府,监管机构和组织为信息隐私和安全提供了法律,法规和指南,以确保正确处理数据。这些通常以自然语言格式指定,包含默认要求和例外,并且通常不明确。此外,在软件开发中还需要考虑相互关注的问题,这些问题通常是多层的,并且来自不同的利益相关者,例如司法辖区。;进行的评估表明,该方法和工具可以带来好处,包括区分需求干扰和冲突,异常处理以及在注释文档和目标模型之间导航;尽管该方法的当前局限性包括手动的用户驱动注释步骤,但该方法提供的功能有助于对来自自然语言来源的机密性要求进行早期分析。机密性问题应集成到软件开发的早期阶段,以便于及早发现需求中的缺陷-不完整和不一致。本文提出了研究,以开发一种使用支持​​默认和异常的目标模型检测这些缺陷的方法。目标模型来自自然语言源的注释。还开发了原型工具来支持该方法。

著录项

  • 作者

    Onabajo, Adeniyi.;

  • 作者单位

    University of Victoria (Canada).;

  • 授予单位 University of Victoria (Canada).;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 256 p.
  • 总页数 256
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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