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. 2020 Nov 3;22(11):e18735. doi: 10.2196/18735

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©Yun William Yu, Griffin M Weber. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.11.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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Applying obfuscation to HLL sketches. (a) HLL+Mask: For each bucket, we count the total number of patients (not just the ones who match the query) whose hashes have the same leading 1-indicator. If that number is less than 10, then the bucket is not 10-anonymous, so we do not send the HLL sketch. Instead, we only send a masked aggregate count of the number of patients matching the query. (b) HLL+Shuffle: We do a coordinated random shuffling so the central hub does not know what the original buckets were for the leading 1 indicator. However, the hub can still estimate cardinality in the same way as HLL without obfuscation. HLL: HyperLogLog.