Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Asymptotically Optimal and Private Statistical Estimation
CANS '09 Proceedings of the 8th International Conference on Cryptology and Network Security
Differential privacy for collaborative security
Proceedings of the Third European Workshop on System Security
Airavat: security and privacy for MapReduce
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Privacy-preserving statistical estimation with optimal convergence rates
Proceedings of the forty-third annual ACM symposium on Theory of computing
Differentially Private Empirical Risk Minimization
The Journal of Machine Learning Research
On the relation between differential privacy and quantitative information flow
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
Quantitative information flow and applications to differential privacy
Foundations of security analysis and design VI
Proceedings of the 4th ACM workshop on Security and artificial intelligence
SIAM Journal on Computing
A differentially private estimator for the stochastic Kronecker graph model
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Differential privacy: on the trade-off between utility and information leakage
FAST'11 Proceedings of the 8th international conference on Formal Aspects of Security and Trust
Confidentialising maps of mixed point and diffuse spatial data
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Countering overlapping rectangle privacy attack for moving kNN queries
Information Systems
Analyzing graphs with node differential privacy
TCC'13 Proceedings of the 10th theory of cryptography conference on Theory of Cryptography
A differentially private mechanism of optimal utility for a region of priors
POST'13 Proceedings of the Second international conference on Principles of Security and Trust
On Learning Cluster Coefficient of Private Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Privacy-preserving data exploration in genome-wide association studies
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Differential privacy for functions and functional data
The Journal of Machine Learning Research
Redrawing the boundaries on purchasing data from privacy-sensitive individuals
Proceedings of the 5th conference on Innovations in theoretical computer science
A near-optimal algorithm for differentially-private principal components
The Journal of Machine Learning Research
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We show by means of several examples that robust statistical estimators present an excellent starting point for differentially private estimators. Our algorithms use a new paradigm for differentially private mechanisms, which we call Propose-Test-Release (PTR), and for which we give a formal definition and general composition theorems.