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2023-02-24 12:58:30
2023-02-25
隱私保護(hù)模式挖掘論文在國(guó)際期刊INS在線發(fā)表
課題組關(guān)于聯(lián)邦框架下的隱私保護(hù)頻繁模式挖掘的論文"Privacy-preserving federated mining of frequent itemsets", 在人工智能等領(lǐng)域的權(quán)威期刊Information Sciences (SCI, IF:8.233, JCR Q1, 中科院一區(qū), CCF B) 在線發(fā)表, https://doi.org/10.1016/j.ins.2023.01.002 。本文作者包括Yao Chen (2021級(jí)研究生)、Wensheng Gan教授 (通訊作者) 、Yongdong Wu教授、美國(guó)伊利諾伊大學(xué)芝加哥分校的Philip S. Yu教授。暨南大學(xué)為論文的第一單位, 該研究得到了國(guó)家自然科學(xué)青年基金和面上項(xiàng)目、廣東省基礎(chǔ)與應(yīng)用基礎(chǔ)研究基金、琶洲實(shí)驗(yàn)室青年學(xué)者項(xiàng)目等資助。 Information Sciences 期刊是計(jì)算機(jī)科學(xué)的人工智能領(lǐng)域具有高影響力的國(guó)際學(xué)術(shù)刊物之一，影響因子為8.233，中科院一區(qū)，主要發(fā)表和報(bào)道人工智能、數(shù)據(jù)科學(xué)、機(jī)器學(xué)習(xí)、隱私安全等領(lǐng)域的最新研究進(jìn)展和技術(shù)。
論文題目: Privacy-preserving federated mining of frequent itemsets
文章鏈接：https://www.sciencedirect.com/science/article/pii/S0020025523000026
Authors: Yao Chen (研究生), Wensheng Gan*, Yongdong Wu, and Philip S. Yu
Abstract: In the growing concerns about data privacy and increasingly stringent data security regulations, it is not feasible to directly mine data or share data if the dataset contains private data. Collecting and analyzing data from multiple parties becomes difficult. Federated learning can analyze multiple datasets while preventing the original data from being sent. However, existing federated learning frameworks are based on the Apriori property of mining frequent patterns, which has the disadvantage of low efficiency and multiple scanning datasets. Therefore, to improve mining efficiency, a federated learning framework (named FedFIM) is proposed in this paper. FedFIM collects the noisy responses sent by participants, which are used by the server to reconstruct the noisy dataset. After that, the noisy dataset is applied to the non-Apriori algorithm to mine frequent patterns. In addition, FedFIM incorporates a differential privacy-preserving mechanism into federated learning, which addresses the need for federated modeling and protects data privacy. Experiments show that FedFIM has a shorter running time and better applicability compared to the most advanced benchmark.
