Workshop on Privacy and Confidentiality; July 19-26, 2005,Italy.

该奖项将为参加将于2005年7月9日至15日在意大利贝尔蒂诺罗的博洛尼亚大学住宿中心举行的隐私和保密研讨会的选定参与者提供差旅费和费用。 研讨会召开之际，各国政府和组织正在努力扩大对统计和其他数据库的访问，同时保护医疗和其他行政记录，打击网络基础设施安全漏洞，特别是涉及未经授权的记录链接和个人身份识别和伤害的漏洞。 与统计数据库有关的保密传统由来已久，但不断扩大的网络基础设施提出了新的、更具挑战性的问题，涉及个人、家庭和其他团体及组织的电子数据库的隐私保护问题。 本次研讨会的目标是汇集来自统计和计算机科学界的领先隐私研究人员，分享专业知识，并制定可行的研究目标。 它将（1）通过建立共同理解的术语，目标和方法描述，帮助形成隐私和保密领域的跨学科方法论导向的知识议程，（2）刺激新的跨学科合作，（3）影响保密研究在不同学科中的方向。 调查的具体领域包括找到超越特定问题和模型细节的保密性，披露和隐私的数学/统计严格定义，找到统计效用的最小定义，了解隐私和效用之间的紧张关系，并了解辅助信息（对手已知的“额外”信息）在击败隐私目标中的作用。

This award supports travel and expenses for selected participants in a Workshop on Privacy and Confidentiality, to be held July 9-15, 2005 at the University of Bologna Residential Center in Bertinoro, Italy. The workshop comes at a time when governments and organizations are struggling to expand access to statistical and other databases while simultaneously protecting medical and other administrative records, and combating breaches of cyberinfrastructure security, especially those involving unauthorized record linkage and individual identification and harm. There has been a long tradition of confidentiality associated with statistical databases, but the ever-expanding cyberinfrastructure raises new and far more challenging questions about the protection of privacy associated with electronic databases involving individuals, families and other groups, and organizations. The goal of this workshop is to bring together leading privacy researchers from the statistics and computer science communities to share expertise and map out feasible research goals. It will (1) help shape an interdisciplinary methodologically-oriented intellectual agenda for the area of privacy and confidentiality by establishing commonly understood terminology, goals, and methodological description, (2) stimulate new interdisciplinary collaborations on the topic, and (3) influence the directions that confidentiality research takes in the separate disciplines. Specific areas of investigation include finding mathematically/statistically rigorous definitions of confidentiality, disclosure, and privacy that transcend specific problem and model details, finding minimal definitions of statistical utility, understanding the tension between privacy and utility, and understanding the role of auxiliary information ("extra" information known to the adversary) in defeating privacy objectives.