An adaptive mechanism for accurate query answering under differential privacy
Proceedings of the VLDB Endowment
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
DP-tree: indexing multi-dimensional data under differential privacy (abstract only)
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Low-rank mechanism: optimizing batch queries under differential privacy
Proceedings of the VLDB Endowment
Functional mechanism: regression analysis under differential privacy
Proceedings of the VLDB Endowment
Adaptive differentially private histogram of low-dimensional data
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Differentially private top-k query over MapReduce
Proceedings of the fourth international workshop on Cloud data management
Non-interactive differential privacy: a survey
Proceedings of the First International Workshop on Open Data
Differential private trajectory protection of moving objects
Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming
Optimal error of query sets under the differentially-private matrix mechanism
Proceedings of the 16th International Conference on Database Theory
Efficient and accurate strategies for differentially-private sliding window queries
Proceedings of the 16th International Conference on Extending Database Technology
PrivGene: differentially private model fitting using genetic algorithms
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Mining frequent graph patterns with differential privacy
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
UMicS: from anonymized data to usable microdata
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
DiffR-Tree: a differentially private spatial index for OLAP query
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A privacy-preserving location-based alert system
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Differentially private multi-dimensional time series release for traffic monitoring
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
Understanding hierarchical methods for differentially private histograms
Proceedings of the VLDB Endowment
Proceedings of the 4th ACM conference on Data and application security and privacy
Privacy-preserving publication of provenance workflows
Proceedings of the 4th ACM conference on Data and application security and privacy
Differentially private histogram publication
The VLDB Journal — The International Journal on Very Large Data Bases
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Differential privacy has recently emerged as the de facto standard for private data release. This makes it possible to provide strong theoretical guarantees on the privacy and utility of released data. While it is well-understood how to release data based on counts and simple functions under this guarantee, it remains to provide general purpose techniques to release data that is useful for a variety of queries. In this paper, we focus on spatial data such as locations and more generally any multi-dimensional data that can be indexed by a tree structure. Directly applying existing differential privacy methods to this type of data simply generates noise. We propose instead the class of ``private spatial decompositions'': these adapt standard spatial indexing methods such as quad trees and kd-trees to provide a private description of the data distribution. Equipping such structures with differential privacy requires several steps to ensure that they provide meaningful privacy guarantees. Various basic steps, such as choosing splitting points and describing the distribution of points within a region, must be done privately, and the guarantees of the different building blocks composed to provide an overall guarantee. Consequently, we expose the design space for private spatial decompositions, and analyze some key examples. A major contribution of our work is to provide new techniques for parameter setting and post-processing the output to improve the accuracy of query answers. Our experimental study demonstrates that it is possible to build such decompositions efficiently, and use them to answer a variety of queries privately with high accuracy.