PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response
Randomized response mechanisms for guaranteeing crowdsourcing data privacy have attracted scholarly attention; aggregators can ensure privacy by collecting only randomized data, and individuals can have plausible deniability regarding their responses. With these mechanisms, analysts employed by orga...
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doaj-442f16c25f914b359a86a40bb82ca6312021-03-29T21:33:45ZengIEEEIEEE Access2169-35362018-01-016769707698310.1109/ACCESS.2018.28845118555987PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized ResponseYao-Tung Tsou0https://orcid.org/0000-0002-7324-5135Bo-Cheng Lin1Department of Communications Engineering, Feng Chia University, Taichung, TaiwanDepartment of Communications Engineering, Feng Chia University, Taichung, TaiwanRandomized response mechanisms for guaranteeing crowdsourcing data privacy have attracted scholarly attention; aggregators can ensure privacy by collecting only randomized data, and individuals can have plausible deniability regarding their responses. With these mechanisms, analysts employed by organizations can still make predictions and conduct analyses using the randomized data. Existing randomized response-based data collection solutions have severely restricted functionality and usability, resulting in impractical and inefficient systems. Therefore, we developed a randomized response-based privacy-preserving crowdsourcing data collection and analysis mechanism. We designed a complementary randomized response (C-RR) method to guarantee individuals' data privacy and to preserve features from the original data for analysis. We formalized a machine learning framework; our proposed method uses randomized data in the form of binary vectors to generate a learning network. Extensive experiments on real-world data sets demonstrated that our heavy-hitters estimation scheme, which applies C-RR and our data learning model, significantly outperformed existing estimation schemes in terms of data analysis.https://ieeexplore.ieee.org/document/8555987/Randomized responselocal differential privacydata analysisrandomized data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yao-Tung Tsou Bo-Cheng Lin |
spellingShingle |
Yao-Tung Tsou Bo-Cheng Lin PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response IEEE Access Randomized response local differential privacy data analysis randomized data |
author_facet |
Yao-Tung Tsou Bo-Cheng Lin |
author_sort |
Yao-Tung Tsou |
title |
PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response |
title_short |
PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response |
title_full |
PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response |
title_fullStr |
PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response |
title_full_unstemmed |
PPDCA: Privacy-Preserving Crowdsourcing Data Collection and Analysis With Randomized Response |
title_sort |
ppdca: privacy-preserving crowdsourcing data collection and analysis with randomized response |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
Randomized response mechanisms for guaranteeing crowdsourcing data privacy have attracted scholarly attention; aggregators can ensure privacy by collecting only randomized data, and individuals can have plausible deniability regarding their responses. With these mechanisms, analysts employed by organizations can still make predictions and conduct analyses using the randomized data. Existing randomized response-based data collection solutions have severely restricted functionality and usability, resulting in impractical and inefficient systems. Therefore, we developed a randomized response-based privacy-preserving crowdsourcing data collection and analysis mechanism. We designed a complementary randomized response (C-RR) method to guarantee individuals' data privacy and to preserve features from the original data for analysis. We formalized a machine learning framework; our proposed method uses randomized data in the form of binary vectors to generate a learning network. Extensive experiments on real-world data sets demonstrated that our heavy-hitters estimation scheme, which applies C-RR and our data learning model, significantly outperformed existing estimation schemes in terms of data analysis. |
topic |
Randomized response local differential privacy data analysis randomized data |
url |
https://ieeexplore.ieee.org/document/8555987/ |
work_keys_str_mv |
AT yaotungtsou ppdcaprivacypreservingcrowdsourcingdatacollectionandanalysiswithrandomizedresponse AT bochenglin ppdcaprivacypreservingcrowdsourcingdatacollectionandanalysiswithrandomizedresponse |
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1724192628969308160 |