Why Data Anonymization Has Not Taken Off

Matthew J. Schneider (), James Bailie () and Dawn Iacobucci ()
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Matthew J. Schneider: Drexel University
James Bailie: Harvard University
Dawn Iacobucci: Vanderbilt University

Customer Needs and Solutions, 2025, vol. 12, issue 1, No 9, 8 pages

Abstract: Abstract Companies are looking to data anonymization research – including differential private and synthetic data methods – for simple and straightforward compliance solutions. But data anonymization has not taken off in practice because it is anything but simple to implement. For one, it requires making complex choices which are case dependent, such as the domain of the dataset to anonymize; the units to protect; the scope where the data protection should extend to; and the standard of protection. Each variation of these choices changes the very meaning, as well as the practical implications, of differential privacy (or of any other measure of data anonymization). Yet differential privacy is frequently being branded as the same privacy guarantee regardless of variations in these choices. Some data anonymization methods can be effective, but only when the insights required are much larger than the unit of protection. Given that businesses care about profitability, any solution must preserve the patterns between a firm’s data and that profitability. As a result, data anonymization solutions usually need to be bespoke and case-specific, which reduces their scalability. Companies should not expect easy wins, but rather recognize that anonymization is just one approach to data privacy with its own particular advantages and drawbacks, while the best strategies jointly leverage the full range of approaches to data privacy and security in combination.

Keywords: Data privacy; Statistical disclosure control; Differential privacy; Information privacy; Data anonymization; Reidentification; Deidentification (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40547-025-00158-5

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