Differential Privacy via Wavelet Transforms

Authors:
Xiaokui Xiao;Guozhang Wang;Johannes Gehrke
Affiliations:
Nanyang Technological University, Singapore;Cornell University, Ithaca;Cornell University, Ithaca
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2011

Citing 0
Cited 25

Sharing graphs using differentially private graph models

Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
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
DuoWave: Mitigating the curse of dimensionality for uncertain data

Data & Knowledge Engineering
Differentially private search log sanitization with optimal output utility

Proceedings of the 15th International Conference on Extending Database Technology
Differential identifiability

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PrivBasis: frequent itemset mining with 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
Real-time aggregate monitoring with differential privacy

Proceedings of the 21st ACM international conference on Information and knowledge management
Recursive partitioning and summarization: a practical framework for differentially private data publishing

Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security
Non-interactive differential privacy: a survey

Proceedings of the First International Workshop on Open Data
On differentially private frequent itemset mining

Proceedings of the VLDB Endowment
A propagation model for provenance views of public/private workflows

Proceedings of the 16th International Conference on Database Theory
Optimal error of query sets under the differentially-private matrix mechanism

Proceedings of the 16th International Conference on Database Theory
A learning theory approach to noninteractive database privacy

Journal of the ACM (JACM)
A privacy framework: indistinguishable privacy

Proceedings of the Joint EDBT/ICDT 2013 Workshops
Mining frequent graph patterns with differential privacy

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Practical differential privacy via grouping and smoothing

Proceedings of the VLDB Endowment
DiffR-Tree: a differentially private spatial index for OLAP query

WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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
A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard

Computer Methods and Programs in Biomedicine
A general framework for privacy preserving data publishing

Knowledge-Based Systems
Understanding hierarchical methods for differentially private histograms

Proceedings of the VLDB Endowment
A generic and distributed privacy preserving classification method with a worst-case privacy guarantee

Distributed and Parallel Databases

Quantified Score

Hi-index	0.00

Visualization

Abstract

Privacy-preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, \epsilon-differential privacy provides the strongest privacy guarantee. Existing data publishing methods that achieve \epsilon-differential privacy, however, offer little data utility. In particular, if the output data set is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. In this paper, we develop a data publishing technique that ensures \epsilon-differential privacy while providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution.