In this work, we introduce a differentially private method for generating synthetic data from
vertically partitioned data,\emph {ie}, where data of the same individuals is distributed across …

Whether the goal is to estimate the number of people that live in a congressional district, to
estimate the number of individuals that have died in an armed conflict, or to disambiguate …

Abstract For datasets in Collaborative Filtering (CF) recommendations, even if the identifier
is deleted and some trivial perturbation operations are applied to ratings before they are …

当今时代, 随着大数据技术的飞速发展和数据量的持续增加, 大量数据不断被不同的公司或者
机构收集, 把来自不同公司或机构的数据聚合起来并发布, 有助于更好地提供服务, 支持决策 …

Today there is necessity to integrate and share data from variety of data sources for mutual
benefits and analysis purpose. Moreover, data integration involves matching of schema …

Given a set of local datasets held by multiple parties, we study the problem of learning
marginals over the integrated dataset while satisfying differential privacy for each local …

Differential privacy (DP) is widely employed for the private data release in the single-party
scenario. Data utility could be degraded with noise generated by ubiquitous data correlation …

摘要保护隐私的位置判断是一种具体的安全多方计算几何问题, 这种问题是指各个参与者位于
平面或者空间中的一个位置, 在保持各自输入隐私的条件下, 判断他们之间的相对位置 …

In the last decade, several approaches concerning private data release for data mining have
been proposed. Data mashup, on the other hand, has recently emerged as a mechanism for …

In recent years, an increasing amount of data is collected in different and often, not
cooperative, databases. The problem of privacy-preserving, distributed calculations over …