Preventing Privacy Disclosure from Hostility Attack Base on Associated Attributes

Zhao, Yushan; Yang, Gaoming; Ge, Baozhen

doi:10.1007/978-3-030-25128-4_163

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent graph based privacy research [26]- [29] construct the relations among individuals but mainly focus on the graphical statistics. Some other works [30]- [32] in the data release community [33]- [35] also noticed the detriment that data correlation brings in and formalized diverse DP variants. However, we emphasize that their claimed data correlation is virtually different from pairwise data.…”

Section: A Privacy Strategiesmentioning

confidence: 99%

Secure Metric Learning via Differential Pairwise Privacy

Li¹,

Pan²,

Sui³

et al. 2020

Preprint

View full text Add to dashboard Cite

Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of individuals given a set of correctly labeled pairwise data by domain experts. These important and precisely-labeled pairwise data are often highly sensitive in real world (e.g., patients similarity). This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning.Unlike traditional differential privacy which only applies to independent samples, thus cannot be used for pairwise data, DPP successfully deals with this problem by reformulating the worst case. Specifically, given the pairwise data, we reveal all the involved correlations among pairs in the constructed undirected graph. DPP is then formalized that defines what kind of DML algorithm is private to preserve pairwise data. After that, a case study employing the contrastive loss is exhibited to clarify the details of implementing a DPP-DML algorithm. Particularly, the sensitivity reduction technique is proposed to enhance the utility of the output distance metric. Experiments both on a toy dataset and benchmarks demonstrate that the proposed scheme achieves pairwise data privacy without compromising the output performance much (Accuracy declines less than 0.01 throughout all benchmark datasets when the privacy budget is set at 4).

show abstract