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Self-adaptive Privacy Concern Detection for User-generated Content
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Database and Data Mining Group)ORCID-id: 0000-0001-8820-2405
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Database and Data Mining Group)
2018 (Engelska)Ingår i: Proceedings of the 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2018, Cornell University Library, arXiv.org , 2018Konferensbidrag, Publicerat paper (Övrigt vetenskapligt)
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).

Ort, förlag, år, upplaga, sidor
Cornell University Library, arXiv.org , 2018.
Nyckelord [en]
privacy-guaranteed data analysis, deep learning, multi-layer perceptron
Nationell ämneskategori
Språkteknologi (språkvetenskaplig databehandling)
Identifikatorer
URN: urn:nbn:se:umu:diva-146470OAI: oai:DiVA.org:umu-146470DiVA, id: diva2:1196463
Konferens
19th International Conference on Computational Linguistics and Intelligent Text Processing, Hanoi, Vietnam, March 18-24, 2018
Projekt
Privacy-aware Data FederationTillgänglig från: 2018-04-10 Skapad: 2018-04-10 Senast uppdaterad: 2019-10-29Bibliografiskt granskad
Ingår i avhandling
1. Privacy-awareness in the era of Big Data and machine learning
Öppna denna publikation i ny flik eller fönster >>Privacy-awareness in the era of Big Data and machine learning
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[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.

Ort, förlag, år, upplaga, sidor
Umeå: Department of computing science, Umeå University, 2019. s. 42
Serie
Report / UMINF, ISSN 0348-0542 ; 19.06
Nyckelord
Diferential Privacy, Machine Learning, Deep Learning, Big Data
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
datalogi
Identifikatorer
urn:nbn:se:umu:diva-162182 (URN)9789178551101 (ISBN)
Presentation
2019-09-09, 23:40 (Engelska)
Handledare
Tillgänglig från: 2019-08-22 Skapad: 2019-08-15 Senast uppdaterad: 2019-08-26Bibliografiskt granskad

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