Optimal numberings of an N N array
SIAM Journal on Algebraic and Discrete Methods
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Towards Optimal Locality in Mesh-Indexings
FCT '97 Proceedings of the 11th International Symposium on Fundamentals of Computation Theory
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the forty-first annual ACM symposium on Theory of computing
Privacy: Theory meets Practice on the Map
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Optimizing linear counting queries under differential privacy
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Differentially private data release through multidimensional partitioning
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
Boosting the accuracy of differentially private histograms through consistency
Proceedings of the VLDB Endowment
An agent-based approach to care in independent living
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Differential Privacy via Wavelet Transforms
IEEE Transactions on Knowledge and Data Engineering
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Differentially Private Spatial Decompositions
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
On the metric properties of discrete space-filling curves
IEEE Transactions on Image Processing
Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security
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We want to publish low-dimensional points, for example 2D spatial points, in a differentially private manner. Most existing mechanisms publish noisy frequency counts of points in a fixed predefined partition. Arguably, histograms with adaptive partition, for example V-optimal and equi-depth histograms, which have smaller bin-widths in denser regions, would provide more statistical information. However, as the adaptive partitions leak significant information about the dataset, it is not clear how differentially private partitions can be published accurately. In this paper, we propose a simple method based on the observation that the sensitivity of publishing the sorted sequence of a dataset is independent of the size of dataset. Together with isotonic regression, the dataset can be reconstructed with high accuracy. One advantage of the proposed method is its simplicity, in the sense that there are only a few parameters to be determined. Furthermore, the parameters can be estimated solely from the privacy requirement ε and the total number of points, and hence do not leak information about the data. Although the parameters are chosen to minimize the earth mover's distance between the published data and original data, empirical studies show that the proposed method also achieves high accuracy w.r.t. to some other measurements, for example range query and order statistics.