1. LDP相关基础论文


[1] Erlingsson, Úlfar, Pihur V , Korolova A . RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response[J]. 2014.

[2] Fanti G , Pihur V , Erlingsson, Úlfar. Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries[J]. Proceedings on Privacy Enhancing Technologies, 2015, 2016(3):41-61.

1.2 Random Matrix Projection

[3] Bassily R , Smith A . Local, Private, Efficient Protocols for Succinct Histograms[J]. 2015:127-135.

1.3 LoPub

[4] Ren X , Yu C M , Yu W , et al. LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy [arXiv][J]. arXiv.

1.4 Framework of LDP

[5] Wang T, Blocki J, Li N, et al. Locally Differentially Private Protocols for Frequency Estimation[C]. usenix security symposium, 2017: 729-745.

2. 基于LDP的数据挖掘算法相关论文

2.1 频繁数据挖掘

[6] Wang T , Li N , Jha S . Locally Differentially Private Frequent Itemset Mining[C]// Locally Differentially Private Frequent Itemset Mining. 0.

2.2 边缘概率表发布

[7] Qardaji W H, Yang W, Li N, et al. PriView: practical differentially private release of marginal contingency tables[C]. international conference on management of data, 2014: 1435-1446.

[8] Zhang Z, Wang T, Li N, et al. CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy[C]. computer and communications security, 2018: 212-229. https://doi.org/10.1145/3243734.3243742