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A Differentially Private Query Processing Mechanism using a Batch Strategy within a Limited Privacy Budget

Minsuc Kang, Kangsoo Jung, Seog Park

http://doi.org/10.5626/JOK.2018.45.7.708

A differential privacy has the advantage of being able to protect information regardless of the attacker’s prior knowledge. However, it has a disadvantage in that each query consumes privacy budget. The larger the privacy budget applied to the query, the more accurate are the query results. However it increases the privacy budget consumption and creates a limitation in the query processing limitation. On the other hand, if the privacy budget allocated to each query is too small, the noise becomes too much. This causes the query result to become inaccurate, and this, in turn causes the data utility to deteriorate. In this paper, we propose a batch strategy that reorders differentially private query processing in interactive environment. The proposed technique uses less privacy budget while it guarantees the data utility.

Differentially Private k-Means Clustering based on Dynamic Space Partitioning using a Quad-Tree

Hanjun Goo, Woohwan Jung, Seongwoong Oh, Suyong Kwon, Kyuseok Shim

http://doi.org/10.5626/JOK.2018.45.3.288

There have recently been several studies investigating how to apply a privacy preserving technique to publish data. Differential privacy can protect personal information regardless of an attacker’s background knowledge by adding probabilistic noise to the original data. To perform differentially private k-means clustering, the existing algorithm builds a differentially private histogram and performs the k-means clustering. Since it constructs an equi-width histogram without considering the distribution of data, there are many buckets to which noise should be added. We propose a k-means clustering algorithm using a quad-tree that captures the distribution of data by using a small number of buckets. Our experiments show that the proposed algorithm shows better performance than the existing algorithm.


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