Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the 16th international conference on World Wide Web
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Scalable modeling of real graphs using Kronecker multiplication
Proceedings of the 24th international conference on Machine learning
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Link privacy in social networks
Proceedings of the 17th ACM conference on Information and knowledge management
Differential privacy and robust statistics
Proceedings of the forty-first annual ACM symposium on Theory of computing
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Accurate Estimation of the Degree Distribution of Private Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A Differentially Private Graph Estimator
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Preserving the privacy of sensitive relationships in graph data
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
Sharing graphs using differentially private graph models
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
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
| Hi-index | 0.00 |
We consider the problem of making graph databases such as social networks available to researchers for knowledge discovery while providing privacy to the participating entities. We use a parametric graph model, the stochastic Kronecker graph model, to model the observed graph and construct an estimator of the "true parameter" in a way that both satisfies the rigorous requirements of differential privacy and demonstrates experimental utility on several important graph statistics. The estimator, which may then be published, defines a probability distribution on graphs. Sampling such a distribution yields a synthetic graph that mimics important properties of the original sensitive graph and consequently, could be useful for knowledge discovery.