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Vu, X.-S., Tran N., S. & Lili, J. (2019). dpUGC: Learn Differentially Private Representation for User Generated Contents. In: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019: . Paper presented at 20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France, April 7-13, 2019.
Open this publication in new window or tab >>dpUGC: Learn Differentially Private Representation for User Generated Contents
2019 (English)In: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning differentially private embedding models are both framework- and dataindependent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.

National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:umu:diva-160887 (URN)
Conference
20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France, April 7-13, 2019
Available from: 2019-06-25 Created: 2019-06-25 Last updated: 2019-08-22
Vu, X.-S., Santra, A., Chakravarthy, S. & Lili, J. (2019). Generic Multilayer Network Data Analysis with the Fusion of Content and Structure. In: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019: . Paper presented at 20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France, April 7-13, 2019.
Open this publication in new window or tab >>Generic Multilayer Network Data Analysis with the Fusion of Content and Structure
2019 (English)In: Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Multi-feature data analysis (e.g., on Facebook, LinkedIn) is challenging especially if one wants to do it efficiently and retain the flexibility by choosing features of interest for analysis. Features (e.g., age, gender, relationship, political view etc.) can be explicitly given from datasets, but also can be derived from content (e.g., political view based on Facebook posts). Analysis from multiple perspectives is needed to understand the datasets (or subsets of it) and to infer meaningful knowledge. For example, the influence of age, location, and marital status on political views may need to be inferred separately (or in combination). In this paper, we adapt multilayer network (MLN) analysis, a nontraditional approach, to model the Facebook datasets, integrate content analysis, and conduct analysis, which is driven by a list of desired application based queries. Our experimental analysis shows the flexibility and efficiency of the proposed approach when modeling and analyzing datasets with multiple features.

Keywords
Social network analysis, Multilayer networks, Content analysis
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:umu:diva-162572 (URN)
Conference
20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France, April 7-13, 2019
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22
Vu, X.-S., Addi, A.-M., Elmroth, E. & Lili, J. (2019). Graph-based Interactive Data Federation System for Heterogeneous Data Retrieval and Analytics. In: Proceedings of The 30th TheWebConf'19 (formerly WWW), USA: . Paper presented at The Web Conference, San Fransisco, USA, May 13-17, 2019 (pp. 3595-3599). New York, NY, USA: ACM Digital Library
Open this publication in new window or tab >>Graph-based Interactive Data Federation System for Heterogeneous Data Retrieval and Analytics
2019 (English)In: Proceedings of The 30th TheWebConf'19 (formerly WWW), USA, New York, NY, USA: ACM Digital Library, 2019, p. 3595-3599Conference paper, Published paper (Refereed)
Abstract [en]

Given the increasing number of heterogeneous data stored in relational databases, file systems or cloud environment, it needs to be easily accessed and semantically connected for further data analytic. The potential of data federation is largely untapped, this paper presents an interactive data federation system (https://vimeo.com/ 319473546) by applying large-scale techniques including heterogeneous data federation, natural language processing, association rules and semantic web to perform data retrieval and analytics on social network data. The system first creates a Virtual Database (VDB) to virtually integrate data from multiple data sources. Next, a RDF generator is built to unify data, together with SPARQL queries, to support semantic data search over the processed text data by natural language processing (NLP). Association rule analysis is used to discover the patterns and recognize the most important co-occurrences of variables from multiple data sources. The system demonstrates how it facilitates interactive data analytic towards different application scenarios (e.g., sentiment analysis, privacyconcern analysis, community detection).

Place, publisher, year, edition, pages
New York, NY, USA: ACM Digital Library, 2019
Keywords
heterogeneous data federation, RDF, interactive data analysis
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:umu:diva-160892 (URN)10.1145/3308558.3314138 (DOI)978-1-4503-6674-8 (ISBN)
Conference
The Web Conference, San Fransisco, USA, May 13-17, 2019
Available from: 2019-06-25 Created: 2019-06-25 Last updated: 2019-08-22Bibliographically approved
Vu, X.-S. (2019). Privacy-awareness in the era of Big Data and machine learning. (Licentiate dissertation). Umeå: Department of computing science, Umeå University
Open this publication in new window or tab >>Privacy-awareness in the era of Big Data and machine learning
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Alternative title[sv]
Integritetsmedvetenhet i eran av Big Data och maskininlärning
Abstract [en]

Social Network Sites (SNS) such as Facebook and Twitter, have been playing a great role in our lives. On the one hand, they help connect people who would not otherwise be connected before. Many recent breakthroughs in AI such as facial recognition [49] were achieved thanks to the amount of available data on the Internet via SNS (hereafter Big Data). On the other hand, due to privacy concerns, many people have tried to avoid SNS to protect their privacy. Similar to the security issue of the Internet protocol, Machine Learning (ML), as the core of AI, was not designed with privacy in mind. For instance, Support Vector Machines (SVMs) try to solve a quadratic optimization problem by deciding which instances of training dataset are support vectors. This means that the data of people involved in the training process will also be published within the SVM models. Thus, privacy guarantees must be applied to the worst-case outliers, and meanwhile data utilities have to be guaranteed.

For the above reasons, this thesis studies on: (1) how to construct data federation infrastructure with privacy guarantee in the big data era; (2) how to protect privacy while learning ML models with a good trade-off between data utilities and privacy. To the first point, we proposed different frameworks em- powered by privacy-aware algorithms that satisfied the definition of differential privacy, which is the state-of-the-art privacy-guarantee algorithm by definition. Regarding (2), we proposed different neural network architectures to capture the sensitivities of user data, from which, the algorithm itself decides how much it should learn from user data to protect their privacy while achieves good performance for a downstream task. The current outcomes of the thesis are: (1) privacy-guarantee data federation infrastructure for data analysis on sensitive data; (2) privacy-guarantee algorithms for data sharing; (3) privacy-concern data analysis on social network data. The research methods used in this thesis include experiments on real-life social network dataset to evaluate aspects of proposed approaches.

Insights and outcomes from this thesis can be used by both academic and industry to guarantee privacy for data analysis and data sharing in personal data. They also have the potential to facilitate relevant research in privacy-aware representation learning and related evaluation methods.

Place, publisher, year, edition, pages
Umeå: Department of computing science, Umeå University, 2019. p. 42
Series
Report / UMINF, ISSN 0348-0542 ; 19.06
Keywords
Diferential Privacy, Machine Learning, Deep Learning, Big Data
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-162182 (URN)9789178551101 (ISBN)
Presentation
2019-09-09, 23:40 (English)
Supervisors
Available from: 2019-08-22 Created: 2019-08-15 Last updated: 2019-08-26Bibliographically approved
Tran, S. N., Zhang, Q., Nguyen, A., Vu, X.-S. & Ngo, S. (2018). Improving Recurrent Neural Networks with Predictive Propagation for Sequence Labelling. In: Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I. Paper presented at The 25th International Conference on Neural Information Processing (ICONIP-2018), Siem Reap, Cambodia, December 13-16, 2018 (pp. 452-462). Springer
Open this publication in new window or tab >>Improving Recurrent Neural Networks with Predictive Propagation for Sequence Labelling
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2018 (English)In: Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I, Springer, 2018, p. 452-462Conference paper, Published paper (Refereed)
Abstract [en]

Recurrent neural networks (RNNs) is a useful tool for sequence labelling tasks in natural language processing. Although in practice RNNs suffer a problem of vanishing/exploding gradient, their compactness still offers efficiency and make them less prone to overfitting. In this paper we show that by propagating the prediction of previous labels we can improve the performance of RNNs while keeping the number of parameters in RNNs unchanged and adding only one more step for inference. As a result, the models are still more compact and efficient than other models with complex memory gates. In the experiment, we evaluate the idea on optical character recognition and Chunking which achieve promising results.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science ; vol 11301
Keywords
Recurrent neural networks, NLP, Sequence labelling
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-154482 (URN)10.1007/978-3-030-04167-0_41 (DOI)978-3-030-04166-3 (ISBN)978-3-030-04167-0 (ISBN)
Conference
The 25th International Conference on Neural Information Processing (ICONIP-2018), Siem Reap, Cambodia, December 13-16, 2018
Available from: 2018-12-19 Created: 2018-12-19 Last updated: 2019-08-07Bibliographically approved
Vu, X.-S., Flekova, L., Lili, J. & Gurevych, I. (2018). Lexical-semantic resources: yet powerful resources for automatic personality classification. In: Francis Bond, Takayuki Kuribayashi, Christiane Fellbaum, Piek Vossen (Ed.), Proceedings of the 9th Global WordNet Conference (GWC 2018): . Paper presented at The 9th Global WordNet Conference GWC2018, Singapore, January 8-12, 2018 (pp. 173-182). Singapore: Nanyang Technological University (NTU)
Open this publication in new window or tab >>Lexical-semantic resources: yet powerful resources for automatic personality classification
2018 (English)In: Proceedings of the 9th Global WordNet Conference (GWC 2018) / [ed] Francis Bond, Takayuki Kuribayashi, Christiane Fellbaum, Piek Vossen, Singapore: Nanyang Technological University (NTU) , 2018, , p. 10p. 173-182Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.

Place, publisher, year, edition, pages
Singapore: Nanyang Technological University (NTU), 2018. p. 10
Keywords
Personality Profiling
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-144672 (URN)978-981-11-7087-4 (ISBN)
Conference
The 9th Global WordNet Conference GWC2018, Singapore, January 8-12, 2018
Projects
Privacy-aware Data Federation
Available from: 2018-02-09 Created: 2018-02-09 Last updated: 2019-08-22Bibliographically approved
Vu, T., Nguyen, D. Q., Vu, X.-S., Nguyen, D. Q., Catt, M. & Trenell, M. (2018). NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter. In: Proceedings of The 12th International Workshop on Semantic Evaluation: . Paper presented at the 12nd International Workshop on Semantic Evaluation (SemEval-2018). New Orleans, Louisiana, USA: The Association for Computational Linguistics
Open this publication in new window or tab >>NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
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2018 (English)In: Proceedings of The 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, USA: The Association for Computational Linguistics , 2018Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features.  Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at fifth in terms of the accuracy metric and the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitter

Place, publisher, year, edition, pages
New Orleans, Louisiana, USA: The Association for Computational Linguistics, 2018
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:umu:diva-147650 (URN)10.18653/v1/S18-1085 (DOI)978-1-948087-20-9 (ISBN)
Conference
the 12nd International Workshop on Semantic Evaluation (SemEval-2018)
Available from: 2018-05-12 Created: 2018-05-12 Last updated: 2019-06-18Bibliographically approved
Vu, X.-S. & Lili, J. (2018). Self-adaptive Privacy Concern Detection for User-generated Content. In: Proceedings of the 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2018: . Paper presented at 19th International Conference on Computational Linguistics and Intelligent Text Processing, Hanoi, Vietnam, March 18-24, 2018.
Open this publication in new window or tab >>Self-adaptive Privacy Concern Detection for User-generated Content
2018 (English)In: Proceedings of the 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2018, 2018Conference paper (Other academic)
Abstract [en]

To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual’s sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).

Series
Lecture Notes in Computer Science (LNCS),
Keywords
privacy-guaranteed data analysis, deep learning, multi-layer perceptron
National Category
Language Technology (Computational Linguistics)
Identifiers
urn:nbn:se:umu:diva-146470 (URN)
Conference
19th International Conference on Computational Linguistics and Intelligent Text Processing, Hanoi, Vietnam, March 18-24, 2018
Projects
Privacy-aware Data Federation
Available from: 2018-04-10 Created: 2018-04-10 Last updated: 2019-08-22
Vu, X.-S., Jiang, L., Brändström, A. & Elmroth, E. (2017). Personality-based Knowledge Extraction for Privacy-preserving Data Analysis. In: K-CAP 2017 - Proceedings of the Knowledge Capture Conference: . Paper presented at K-CAP 2017: The 9th International Conference on Knowledge Capture, Austin, Texas, December 4-6, 2017. Austin, TX, USA: ACM Digital Library, Article ID 45.
Open this publication in new window or tab >>Personality-based Knowledge Extraction for Privacy-preserving Data Analysis
2017 (English)In: K-CAP 2017 - Proceedings of the Knowledge Capture Conference, Austin, TX, USA: ACM Digital Library, 2017, article id 45Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a differential privacy preserving approach, which extracts personality-based knowledge to serve privacy guarantee data analysis on personal sensitive data. Based on the approach, we further implement an end-to-end privacy guarantee system, KaPPA, to provide researchers iterative data analysis on sensitive data. The key challenge for differential privacy is determining a reasonable amount of privacy budget to balance privacy preserving and data utility. Most of the previous work applies unified privacy budget to all individual data, which leads to insufficient privacy protection for some individuals while over-protecting others. In KaPPA, the proposed personality-based privacy preserving approach automatically calculates privacy budget for each individual. Our experimental evaluations show a significant trade-off of sufficient privacy protection and data utility.

Place, publisher, year, edition, pages
Austin, TX, USA: ACM Digital Library, 2017
Keywords
Differential Privacy, Privacy-preserving Data Analysis
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-143228 (URN)10.1145/3148011.3154479 (DOI)978-1-4503-5553-7 (ISBN)
Conference
K-CAP 2017: The 9th International Conference on Knowledge Capture, Austin, Texas, December 4-6, 2017
Projects
Privacy-aware data federation
Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2019-08-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8820-2405

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