Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals,such as diseases of patients,the credit rating of a customer,and the salary of an employee.Mea...Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals,such as diseases of patients,the credit rating of a customer,and the salary of an employee.Meanwhile,certain information is required to be published.In this paper,we consider data-publishing applications where the publisher specifies both sensitive information and shared information.An adversary can infer the real value of a sensitive entry with a high confidence by using publishing data.The goal is to protect sensitive information in the presence of data inference using derived association rules on publishing data.We formulate the inference attack framework,and develop complexity results.We show that computing a safe partial table is an NP-hard problem.We classify the general problem into subcases based on the requirements of publishing information,and propose algorithms for finding a safe partial table to publish.We have conducted an empirical study to evaluate these algorithms on real data.The test results show that the proposed algorithms can produce approximate maximal published data and improve the performance of existing algorithms.展开更多
On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medi...On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medical researchers,and science policy experts to examine how data and artificial intelligence(AI)are reshaping scholarly publishing.Two keynote speeches set the stage:the first analyzed the opportunities for hospital-based research arising from new journal policies,data infrastructure,and enabling technologies;the second introduced the latest advances in general AI and their implications for academic publishing security and integrity.展开更多
基金Supported by the Program for New Century Excellent Talents in Universities(Grant No.NCET-06-0290)the National Natural Science Foundation of China(Grant Nos.60828004,60503036)the Fok Ying Tong Education Foundation Award(Grant No.104027)
文摘Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals,such as diseases of patients,the credit rating of a customer,and the salary of an employee.Meanwhile,certain information is required to be published.In this paper,we consider data-publishing applications where the publisher specifies both sensitive information and shared information.An adversary can infer the real value of a sensitive entry with a high confidence by using publishing data.The goal is to protect sensitive information in the presence of data inference using derived association rules on publishing data.We formulate the inference attack framework,and develop complexity results.We show that computing a safe partial table is an NP-hard problem.We classify the general problem into subcases based on the requirements of publishing information,and propose algorithms for finding a safe partial table to publish.We have conducted an empirical study to evaluate these algorithms on real data.The test results show that the proposed algorithms can produce approximate maximal published data and improve the performance of existing algorithms.
文摘On April 26,2025,the Second Tsinghua Medicine Journal Innovation Conference convened in Beijing.Centered on the theme“AI-driven Academic:Shaping the Next Frontier”the Conference brought together journal editors,medical researchers,and science policy experts to examine how data and artificial intelligence(AI)are reshaping scholarly publishing.Two keynote speeches set the stage:the first analyzed the opportunities for hospital-based research arising from new journal policies,data infrastructure,and enabling technologies;the second introduced the latest advances in general AI and their implications for academic publishing security and integrity.
文摘开放科学是促进科学研究的透明度、开放性及可重复性的实践活动。为评估我国高水平英文医学期刊对开放科学实践的认可程度,从6个维度计算各期刊的开放科学评分(open science score,OSS),并进一步采用Spearman相关分析探讨OSS与期刊影响因子(journal impact factor,JIF)、CiteScore、Scimago期刊排名(scimago journal rank,SJR)及标准化影响系数(source normalized impact per paper,SNIP)之间的关系。结果表明:纳入研究的32种期刊中68.75%遵循报告指南,25.00%为强制性要求;65.62%提及临床试验注册政策;在出版伦理与不端行为方面,56.25%未声明遵循国际出版伦理委员会(COPE)发布的指南和最佳实践建议,仅有37.50%期刊对论文进行剽窃或相似性检查;71.87%期刊鼓励作者共享研究数据;尚无任何期刊采取开放型同行评议;所有期刊OSS值中位数为61.66%,其中被开放获取期刊目录(Directory of Open Access Journals,DOAJ)收录期刊的OSS值高于非DOAJ收录期刊(p<0.05);期刊OSS值与JIF、CiteScore、SJR及SNIP均存在中度正相关关系(p<0.05)。综上所述,纳入研究的32种英文医学期刊对开放科学实践的要求总体不高,各个维度政策的认可程度也存在异质性,因此提出今后期刊应重视和实施标准化的开放科学政策、鼓励良好的科学实践的建议。
