Collective protection: Preventing sensitive inferences via integrative transformation

Dalin Zhang, Lina Yao, Kaixuan Chen, Guodong Long, Sen Wang

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

8 Citations (Scopus)


Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic information that can be inferred from the time series sensor data. Existing sensitive information protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive information for training. Considering this gap, we propose a novel user sensitive information protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive information. The presented approach transforms raw sensor data into a new format that has a 'style' (sensitive information) of random noise and a 'content' (desired information) of the raw sensor data, thus is free of user sensitive information for training and able to collectively protect all sensitive information at once. Our implementation and experiments on two real-world multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the protection for all sensitive information at once without requiring the knowledge of users' personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
Number of pages6
Publication dateNov 2019
Article number8970834
ISBN (Electronic)9781728146034
Publication statusPublished - Nov 2019
Externally publishedYes
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: 8 Nov 201911 Nov 2019


Conference19th IEEE International Conference on Data Mining, ICDM 2019
SponsorBaidu, et al., IEEE Computer Society, LinkedIn, MiningLamp Technology., US National Science Foundation (NSF)
SeriesProceedings - IEEE International Conference on Data Mining, ICDM

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • Activity recognition
  • Data transformation
  • Mobile sensor
  • Sensitive inference


Dive into the research topics of 'Collective protection: Preventing sensitive inferences via integrative transformation'. Together they form a unique fingerprint.

Cite this