Privacy preserving sentiment analysis on multiple edge data streams with Apache NiFi |
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Author: | Pandya, Abhinay1; Kostakos, Panos1; Mehmood, Hassan1; |
Organizations: |
1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland 2Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 3.7 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2020061644570 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2020
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Publish Date: | 2020-06-16 |
Description: |
AbstractSentiment analysis, also known as opinion mining, plays a big role in both private and public sector Business Intelligence (BI); it attempts to improve public and customer experience. Nevertheless, de-identified sentiment scores from public social media posts can compromise individual privacy due to their vulnerability to record linkage attacks. Established privacy-preserving methods like k-anonymity, l-diversity and t-closeness are offline models exclusively designed for data at rest. Recently, a number of online anonymization algorithms (CASTLE, SKY, SWAF) have been proposed to complement the functional requirements of streaming applications, but without open-source implementation. In this paper, we present a reusable Apache NiFi dataflow that buffers tweets from multiple edge devices and performs anonymized sentiment analysis in real-time, using randomization. The solution can be easily adapted to suit different scenarios, enabling researchers to deploy custom anonymization algorithms. see all
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ISBN: | 978-1-7281-6735-0 |
ISBN Print: | 978-1-7281-6736-7 |
Pages: | 130 - 133 |
DOI: | 10.1109/EISIC49498.2019.9108851 |
OADOI: | https://oadoi.org/10.1109/EISIC49498.2019.9108851 |
Host publication: |
2019 European Intelligence and Security Informatics Conference (EISIC) |
Conference: |
European Intelligence and Security Informatics Conference |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
Funding: |
This work is (partially) funded by the European Commission grant 770469-CUTLER and 815362-PRINCE |
EU Grant Number: |
(770469) CUTLER - Coastal Urban developmenT through the LEnses of Resiliency |
Copyright information: |
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