Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Privacy-safe network trace sharing via secure queries
Proceedings of the 1st ACM workshop on Network data anonymization
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Airavat: security and privacy for MapReduce
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
A firm foundation for private data analysis
Communications of the ACM
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
Differential privacy for location pattern mining
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Fault-tolerant privacy-preserving statistics
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
BTA: architecture for reusable business tier components with access control
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
On significance of the least significant bits for differential privacy
Proceedings of the 2012 ACM conference on Computer and communications security
Non-interactive differential privacy: a survey
Proceedings of the First International Workshop on Open Data
Privacy by design: a formal framework for the analysis of architectural choices
Proceedings of the third ACM conference on Data and application security and privacy
A theory of pricing private data
Proceedings of the 16th International Conference on Database Theory
Differential privacy in intelligent transportation systems
Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks
πBox: a platform for privacy-preserving apps
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
Differentially private multi-dimensional time series release for traffic monitoring
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
Monitoring web browsing behavior with differential privacy
Proceedings of the 23rd international conference on World wide web
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Privacy Integrated Queries (PINQ) is an extensible data analysis platform designed to provide unconditional privacy guarantees for the records of the underlying data sets. PINQ provides analysts with access to records through an SQL-like declarative language (LINQ) amidst otherwise arbitrary C# code. At the same time, the design of PINQ's analysis language and its careful implementation provide formal guarantees of differential privacy for any and all uses of the platform. PINQ's guarantees require no trust placed in the expertise or diligence of the analysts, broadening the scope for design and deployment of privacy-preserving data analyses, especially by privacy nonexperts.