Privacy-preserving data sharing via probabilistic modelling
Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所）；University of Helsinki（赫尔辛基大学）；University of Manchester（曼彻斯特大学）；Aalto University（阿尔托大学）
Joonas Jälkö, Eemil Lagerspetz, Jari Haukka, Sasu Tarkoma, Antti Honkela, Samuel Kaski
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modelling. This approach transforms the problem of designing the synthetic data into choosing a model for the data, allowing also including prior knowledge, which improves the quality of the synthetic data. We demonstrate empirically, in an epidemiological study, that statistical discoveries can be reliably reproduced from the synthetic data. We expect the method to have broad use in creating high-quality anonymized data twins of key data sets for research.
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research
Department of Computer and Data Sciences（计算机与数据科学系）；Case Western Reserve University（凯斯西储大学）；School of Computer Science（计算机科学学院）；Georgia Institute of Technology（佐治亚理工学院）；Department of Planning and Landscape Architecture（规划与景观建筑系）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Institute for Neuroinformatics, Ruhr University Bochum（波鸿鲁尔大学神经信息学研究所）；Monash University（莫纳什大学）；Case Western Reserve University（凯斯西储大学）；Carenostics（Carenostics公司）
Privacy-preserving Data Sharing on Vertically Partitioned Data
Helsinki Institute for Information Technology HIIT（赫尔辛基信息技术研究所（HIIT））；Aalto University（阿尔托大学）；University of Manchester（曼彻斯特大学）；University of Helsinki（赫尔辛基大学）
