PCPs and the hardness of generating private synthetic data
TCC'11 Proceedings of the 8th conference on Theory of cryptography
Towards privacy for social networks: a zero-knowledge based definition of privacy
TCC'11 Proceedings of the 8th conference on Theory of cryptography
Private data release via learning thresholds
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
A Practical Differentially Private Random Decision Tree Classifier
Transactions on Data Privacy
Differential privacy in data publication and analysis
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Beating randomized response on incoherent matrices
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Unconditional differentially private mechanisms for linear queries
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Optimal private halfspace counting via discrepancy
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Privacy-aware mechanism design
Proceedings of the 13th ACM Conference on Electronic Commerce
Iterative constructions and private data release
TCC'12 Proceedings of the 9th international conference on Theory of Cryptography
Differential privacy and the power of (formalizing) negative thinking
POST'12 Proceedings of the First international conference on Principles of Security and Trust
Low-rank mechanism: optimizing batch queries under differential privacy
Proceedings of the VLDB Endowment
Faster algorithms for privately releasing marginals
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Is privacy compatible with truthfulness?
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Characterizing the sample complexity of private learners
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Non-interactive differential privacy: a survey
Proceedings of the First International Workshop on Open Data
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)
Truthful mechanisms for agents that value privacy
Proceedings of the fourteenth ACM conference on Electronic commerce
Beyond worst-case analysis in private singular vector computation
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Differential privacy for the analyst via private equilibrium computation
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
The geometry of differential privacy: the sparse and approximate cases
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Answering n{2+o(1)} counting queries with differential privacy is hard
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
Probabilistic Relational Reasoning for Differential Privacy
ACM Transactions on Programming Languages and Systems (TOPLAS)
Differential privacy for functions and functional data
The Journal of Machine Learning Research
Faster private release of marginals on small databases
Proceedings of the 5th conference on Innovations in theoretical computer science
Mechanism design in large games: incentives and privacy
Proceedings of the 5th conference on Innovations in theoretical computer science
Redrawing the boundaries on purchasing data from privacy-sensitive individuals
Proceedings of the 5th conference on Innovations in theoretical computer science
Differentially private histogram publication
The VLDB Journal — The International Journal on Very Large Data Bases
Hi-index | 0.00 |
Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved {\em privacy-preserving synopses} of an input database. These are data structures that yield, for a given set $\Q$ of queries over an input database, reasonably accurate estimates of the responses to every query in~$\Q$, even when the number of queries is much larger than the number of rows in the database. Given a {\em base synopsis generator} that takes a distribution on $\Q$ and produces a ``weak'' synopsis that yields ``good'' answers for a majority of the weight in $\Q$, our {\em Boosting for Queries} algorithm obtains a synopsis that is good for all of~$\Q$. We ensure privacy for the rows of the database, but the boosting is performed on the {\em queries}. We also provide the first synopsis generators for arbitrary sets of arbitrary low-sensitivity queries, {\it i.e.}, queries whose answers do not vary much under the addition or deletion of a single row. In the execution of our algorithm certain tasks, each incurring some privacy loss, are performed many times. To analyze the cumulative privacy loss, we obtain an $O(\eps^2)$ bound on the {\em expected} privacy loss from a single $\eps$-\dfp{} mechanism. Combining this with evolution of confidence arguments from the literature, we get stronger bounds on the expected cumulative privacy loss due to multiple mechanisms, each of which provides $\eps$-differential privacy or one of its relaxations, and each of which operates on (potentially) different, adaptively chosen, databases.