Privacy preserving text data encoding and topic modelling

Dinusha Vatsalan, Raghav Bhaskar, Aris Gkoulalas-Divanis, Dimitrios Karapiperis

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


Textual data, such as clinical notes, product or movie reviews in online stores, transcripts, chat records, and business documents, are widely collected nowadays and can be used to support a large spectrum of Big Data applications. At the same time, textual data, collected about individuals or from individuals, can be susceptible to inference attacks that may leak private and/or sensitive information about individuals. The increasing concerns of privacy risks in textual data preclude sharing or exchanging textual data across different parties/organizations for various applications such as record linkage, similar entity matching, natural language processing (NLP), or machine learning on large collections of textual data. This has led to the development of privacy preserving techniques for applying matching, machine learning or NLP techniques on textual data that contain personal and sensitive information about individuals. While cryptographic techniques are highly secure and accurate, they incur significant amount of computational cost for encoding and matching data -- especially textual data -- due to the complex nature of text. In this paper, we propose an efficient textual data encoding and matching algorithm using probabilistic techniques based on counting Bloom filters combined with Differential privacy. We apply our algorithm to a popular use case scenario that involves privacy preserving topic modeling -- a widely used NLP technique -- in order to identify common or collective topics in texts across multiple parties without learning the individual topics of each party, and show its effectiveness in supporting this application. Finally, through extensive experimental evaluation on three large text datasets against a state-of-the-art probabilistic encoding algorithm for privacy preserving LDA topic modelling, we show that our method provides a better privacy-utility trade-off at the cost of more computation complexity and memory space, while still being computationally efficient (log-linear complexity in the size of documents) for Big data compared to cryptographic techniques that have quadratic complexity.
Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9781665439022
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Big Data (Big Data) - Virtual
Duration: 15 Dec 202118 Dec 2021

Publication series

NameIEEE International Conference on Big Data
ISSN (Print)2639-1589


Conference2021 IEEE International Conference on Big Data (Big Data)


  • Differential privacy
  • counting Bloom filters
  • distance-preserving encoding
  • textual data
  • topic models


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