Cornell University, Ithaca, New York, USA.
Ann N Y Acad Sci. 2012 Jul;1260:45-54. doi: 10.1111/j.1749-6632.2012.06630.x.
Society can gain much value from Big Data. We can study census data to learn where to allocate public resources, medical records from hospitals to fight diseases, or data about students and teachers to evaluate the effectiveness of various approaches to learning and teaching. In all of these scenarios, we need to limit statistical disclosure: we want to release accurate statistics about the data while preserving the privacy of the individuals who contributed it. This paper gives an overview of recent advances and open challenges in the field, focusing on methods that probably limit how much an adversary can learn from a data release.
社会可以从大数据中获得很多价值。我们可以研究人口普查数据，了解在哪里分配公共资源；可以研究医院的医疗记录，以对抗疾病；也可以研究学生和教师的数据，以评估各种学习和教学方法的效果。在所有这些场景中，我们都需要限制统计数据的披露：我们希望发布关于数据的准确统计信息，同时保护提供数据的个人的隐私。本文概述了该领域的最新进展和开放挑战，重点介绍了可能限制对手从数据发布中获取信息的方法。
