Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The price of privacy and the limits of LP decoding
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
New Efficient Attacks on Statistical Disclosure Control Mechanisms
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
The Differential Privacy Frontier (Extended Abstract)
TCC '09 Proceedings of the 6th Theory of Cryptography Conference on Theory of Cryptography
An ad omnia approach to defining and achieving private data analysis
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
A firm foundation for private data analysis
Communications of the ACM
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
Differential privacy under continual observation
Proceedings of the forty-second ACM symposium on Theory of computing
Private and continual release of statistics
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Pan-private algorithms via statistics on sketches
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Theory of data stream computing: where to go
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Differentially private billing with rebates
IH'11 Proceedings of the 13th international conference on Information hiding
Private and Continual Release of Statistics
ACM Transactions on Information and System Security (TISSEC)
Formal Verification of Differential Privacy for Interactive Systems (Extended Abstract)
Electronic Notes in Theoretical Computer Science (ENTCS)
Approximately optimal mechanism design via differential privacy
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
A Practical Differentially Private Random Decision Tree Classifier
Transactions on Data Privacy
The power of the dinur-nissim algorithm: breaking privacy of statistical and graph databases
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
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
Private decayed predicate sums on streams
Proceedings of the 16th International Conference on Database Theory
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
Efficient and accurate strategies for differentially-private sliding window queries
Proceedings of the 16th International Conference on Extending Database Technology
Proceedings of the 4th ACM conference on Data and application security and privacy
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Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without compromising the the privacy of any individual [7]. We describe new work [10, 9] that extends differentially private data analysis beyond the traditional setting of a trusted curator operating, in perfect isolation, on a static dataset. We ask • How can we guarantee differential privacy, even against an adversary that has access to the algorithm's internal state, eg, by subpoena? An algorithm that achives this is said to be pan-private. • How can we guarantee differential privacy when the algorithm must continually produce outputs? We call this differential privacy under continual observation. We also consider these requirements in conjunction.