Generating quasi-random sequences from semi-random sources
Journal of Computer and System Sciences
What every computer scientist should know about floating-point arithmetic
ACM Computing Surveys (CSUR)
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
A note on the confinement problem
Communications of the ACM
Pracniques: further remarks on reducing truncation errors
Communications of the ACM
Accuracy and Stability of Numerical Algorithms
Accuracy and Stability of Numerical Algorithms
On the Impossibility of Private Key Cryptography with Weakly Random Keys
CRYPTO '90 Proceedings of the 10th Annual International Cryptology Conference on Advances in Cryptology
On the (Im)possibility of Cryptography with Imperfect Randomness
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Universally utility-maximizing privacy mechanisms
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
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Optimizing linear counting queries under differential privacy
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Communications of the ACM
Handbook of Floating-Point Arithmetic
Handbook of Floating-Point Arithmetic
Differentially-private network trace analysis
Proceedings of the ACM SIGCOMM 2010 conference
Airavat: security and privacy for MapReduce
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
Distance makes the types grow stronger: a calculus for differential privacy
Proceedings of the 15th ACM SIGPLAN international conference on Functional programming
A firm foundation for private data analysis
Communications of the ACM
Differential privacy under fire
SEC'11 Proceedings of the 20th USENIX conference on Security
Privacy-friendly aggregation for the smart-grid
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Differentially private billing with rebates
IH'11 Proceedings of the 13th international conference on Information hiding
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
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
GUPT: privacy preserving data analysis made easy
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Geo-indistinguishability: differential privacy for location-based systems
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
A near-optimal algorithm for differentially-private principal components
The Journal of Machine Learning Research
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We describe a new type of vulnerability present in many implementations of differentially private mechanisms. In particular, all four publicly available general purpose systems for differentially private computations are susceptible to our attack. The vulnerability is based on irregularities of floating-point implementations of the privacy-preserving Laplacian mechanism. Unlike its mathematical abstraction, the textbook sampling procedure results in a porous distribution over double-precision numbers that allows one to breach differential privacy with just a few queries into the mechanism. We propose a mitigating strategy and prove that it satisfies differential privacy under some mild assumptions on available implementation of floating-point arithmetic.