Randomized rounding: a technique for provably good algorithms and algorithmic proofs
Combinatorica - Theory of Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Dependent rounding and its applications to approximation algorithms
Journal of the ACM (JACM)
Approximation Algorithms for the Max-Min Allocation Problem
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
Maximizing submodular set functions subject to multiple linear constraints
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Non-monotone submodular maximization under matroid and knapsack constraints
Proceedings of the forty-first annual ACM symposium on Theory of computing
Injector: Mining Background Knowledge for Data Anonymization
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A utility-theoretic approach to privacy and personalization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
P4P: practical large-scale privacy-preserving distributed computation robust against malicious users
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
Privad: practical privacy in online advertising
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sedic: privacy-aware data intensive computing on hybrid clouds
Proceedings of the 18th ACM conference on Computer and communications security
Randomized rounding for routing and covering problems: experiments and improvements
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
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We present SPARSI, a novel theoretical framework for partitioning sensitive data across multiple non-colluding adversaries. Most work in privacy-aware data sharing has considered disclosing summaries where the aggregate information about the data is preserved, but sensitive user information is protected. Nonetheless, there are applications, including online advertising, cloud computing and crowdsourcing markets, where detailed and fine-grained user data must be disclosed. We consider a new data sharing paradigm and introduce the problem of privacy-aware data partitioning, where a sensitive dataset must be partitioned among k untrusted parties (adversaries). The goal is to maximize the utility derived by partitioning and distributing the dataset, while minimizing the total amount of sensitive information disclosed. The data should be distributed so that an adversary, without colluding with other adversaries, cannot draw additional inferences about the private information, by linking together multiple pieces of information released to her. The assumption of no collusion is both reasonable and necessary in the above application domains that require release of private user information. SPARSI enables us to formally define privacy-aware data partitioning using the notion of sensitive properties for modeling private information and a hypergraph representation for describing the interdependencies between data entries and private information. We show that solving privacy-aware partitioning is, in general, NP-hard, but for specific information disclosure functions, good approximate solutions can be found using relaxation techniques. Finally, we present a local search algorithm applicable to generic information disclosure functions. We conduct a rigorous performance evaluation with real-world and synthetic datasets that illustrates the effectiveness of SPARSI at partitioning sensitive data while minimizing disclosure.