Abstract
In the current era of data driven governance and businesses, data sharing and opening become an essential growth factor. Data intensive organizations are eager to share their data with the public, research institutions and private enterprises. However, data sharing must adhere to data protection laws and regulations, particularly respect the principle of personal data minimization. Statistical Disclosure Control (SDC) is a major technology that aims at minimizing personal information in a data set while retaining the data utility at an acceptable level for data consumers. Despite having many tools developed to automate the process of applying SDC technology, still the majority of organizations are struggling to adapt it. In this contribution, first, we mention the common challenges that data intensive organizations are facing for employing existing SDC tools. Then, we propose a SDC tool set-up, whereby organizations can outsource the anonymization of their microdata sets to a central party safely (i.e., without sharing their raw data). Finally, we present the current status of the study together with a few questions for future research.
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This work was supported by RUAS SiA grant for Scalable and Usable Privacy Preserving Techniques project.
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Amighi, A., Bargh, M.S., Omar, A. (2021). WiP: A Distributed Approach for Statistical Disclosure Control Technologies. In: Tripathy, S., Shyamasundar, R.K., Ranjan, R. (eds) Information Systems Security. ICISS 2021. Lecture Notes in Computer Science(), vol 13146. Springer, Cham. https://doi.org/10.1007/978-3-030-92571-0_9
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