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Perosonalized Differentially Private Location Collection Method with Adaptive GPS Discretization [chapter]

Huichuan Liu, Yong Zeng, Jiale Liu, Zhihong Liu, Jianfeng Ma, Xiaoyan Zhu
2020 Communications in Computer and Information Science  
To protect user privacy, researchers have adopted local differential privacy in data collection process.  ...  Thus, we further improve the optimized binary local hash method, based on personalized differential privacy, to collect user visit frequency of each discretized region.  ...  In 2018, a frequent item set discovery framework, called PrivTrie [11] , based on prefix trees was proposed.  ... 
doi:10.1007/978-981-33-4922-3_13 fatcat:d4velgugonboddipdvnitn3jtm

A Comprehensive Survey on Local Differential Privacy

Xingxing Xiong, Shubo Liu, Dan Li, Zhaohui Cai, Xiaoguang Niu
2020 Security and Communication Networks  
Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while  ...  application fields under LDP.  ...  [68] investigated the locally differentially private frequent itemset discovery over the set-valued data. Wang et al.  ... 
doi:10.1155/2020/8829523 fatcat:xjk3vgyambb5xioc2q5hyr2hua

Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation [article]

Eugene Bagdasaryan, Peter Kairouz, Stefan Mellem, Adrià Gascón, Kallista Bonawitz, Deborah Estrin, Marco Gruteser
2022 arXiv   pre-print
To achieve this, we revisit distributed differential privacy based on recent results in secure multiparty computation, and we design a scalable and adaptive distributed differential privacy approach for  ...  It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high data accuracy and minimizing resource consumption on users' devices.  ...  More broadly, we note that there is a rich body of theoretical work on distribution learning, frequent sequence mining, and heavy-hitter discovery both in the central and local models of DP [1, 8, 10,  ... 
arXiv:2111.02356v2 fatcat:5sgu3qkmfbejrknm2xtlpcb7wq

Private True Data Mining: Differential Privacy Featuring Errors to Manage Internet-of-Things Data

Yuichi Sei, Akihiko Ohsuga
2022 IEEE Access  
‘‘PrivTrie: Effective frequent term discovery under local differential pri- Commun. Netw., vol. 2017, pp. 1–41, Nov. 2017. vacy,’’ in Proc.  ...  ., vol. 8, no. 11, pp. 8836–8853, tion under local differential privacy with small samples,’’ Proc.  ... 
doi:10.1109/access.2022.3143813 fatcat:744x2rlswfewzpkctjpxluibxa

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Teng Wang, Xuefeng Zhang, Jingyu Feng, Xinyu Yang
2020 Sensors  
Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing  ...  Finally, we outline several future research directions under LDP.  ...  Frequent Items Mining Frequent items mining (also known as heavy hitters identification, top-ω hitters mining, or frequent terms discovery) has played important roles in data statistics.  ... 
doi:10.3390/s20247030 pmid:33302517 pmcid:PMC7763193 fatcat:25iufaivynabdftrzq4rzxsz2e

A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis in Crowdsensing [article]

Teng Wang, Xuefeng Zhang, Jingyu Feng, Xinyu Yang
2020 arXiv   pre-print
Local differential privacy (LDP) has been proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting  ...  Finally, we outline several future research directions under LDP.  ...  Frequent Items Mining Frequent items mining (also known as heavy hitters identification, top-ω hitters mining, or frequent terms discovery) has played important roles in data statistics.  ... 
arXiv:2010.05253v2 fatcat:uuts5enifreixjt4lf6yjrepl4

Generation Matrix: An Embeddable Matrix Representation for Hierarchical Trees [article]

Jianping Cai, Ximeng Liu, Jiayin Li, Shuangyue Zhang
2022
Applying Generation Matrix to differential privacy hierarchical tree release, we propose a Generation Matrix-based optimally consistent release algorithm (GMC).  ...  Starting from the local structures to study hierarchical trees is a common research method.  ...  In the differentially private frequent term discovery problem studied by Ning et al. [14] , the hierarchical tree is arbitrary. Therefore, it is impossible to apply Boosting.  ... 
doi:10.48550/arxiv.2201.11297 fatcat:kfvlnpzi25d2pe5wtr4blnkrcu