University of Oulu

A. Pandya et al., "Privacy preserving sentiment analysis on multiple edge data streams with Apache NiFi," 2019 European Intelligence and Security Informatics Conference (EISIC), Oulu, Finland, 2019, pp. 130-133, doi: 10.1109/EISIC49498.2019.9108851

Privacy preserving sentiment analysis on multiple edge data streams with Apache NiFi

Saved in:
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:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-06-16


Sentiment 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

ISBN: 978-1-7281-6735-0
ISBN Print: 978-1-7281-6736-7
Pages: 130 - 133
DOI: 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
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: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.