Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Convex Optimization
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Cryptographically private support vector machines
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th international conference on World Wide Web
Training a Support Vector Machine in the Primal
Neural Computation
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
"I know what you did last summer": query logs and user privacy
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
SVM optimization: inverse dependence on training set size
Proceedings of the 25th international conference on Machine learning
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving classification of vertically partitioned data via random kernels
ACM Transactions on Knowledge Discovery from Data (TKDD)
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Differential privacy and robust statistics
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
Proceedings of the 16th ACM conference on Computer and communications security
Differential privacy with compression
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Proceedings of the forty-second ACM symposium on Theory of computing
Privacy-preserving support vector machine classification
International Journal of Intelligent Information and Database Systems
Differentially private combinatorial optimization
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Bounds on the sample complexity for private learning and private data release
TCC'10 Proceedings of the 7th international conference on Theory of Cryptography
When random sampling preserves privacy
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
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
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Privacy-preserving statistical estimation with optimal convergence rates
Proceedings of the forty-third annual ACM symposium on Theory of computing
Large margin multiclass gaussian classification with differential privacy
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Proceedings of the 4th ACM workshop on Security and artificial intelligence
Differential privacy for location pattern mining
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Differentially-private learning and information theory
Proceedings of the 2012 Joint EDBT/ICDT Workshops
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
Differentially private projected histograms: construction and use for prediction
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Cloud-enabled privacy-preserving collaborative learning for mobile sensing
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Linear dependent types for differential privacy
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
PrivGene: differentially private model fitting using genetic algorithms
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Differential privacy for functions and functional data
The Journal of Machine Learning Research
Differential privacy based on importance weighting
Machine Learning
A near-optimal algorithm for differentially-private principal components
The Journal of Machine Learning Research
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Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance.