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l-Diversity: Privacy Beyond k-Anonymity
TLDR
This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.
L-diversity: privacy beyond k-anonymity
TLDR
This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.
No free lunch in data privacy
TLDR
This paper argues that privacy of an individual is preserved when it is possible to limit the inference of an attacker about the participation of the individual in the data generating process, different from limiting the inference about the presence of a tuple.
Privacy: Theory meets Practice on the Map
In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community. The
Pufferfish: A framework for mathematical privacy definitions
TLDR
The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a given application and is introduced to allow experts in an application domain to develop rigorous privacy definitions for their data sharing needs.
Finding connected components in map-reduce in logarithmic rounds
TLDR
Two efficient map-reduce algorithms are proposed: Hash-Greater-to-Min, which is a randomized algorithm based on PRAM techniques, requiring O(log n) rounds and O(|V | + |E|) communication per round, and Hash-to theMin,which is a novel algorithm, provably finishing in O( log n) iterations for path graphs.
A rigorous and customizable framework for privacy
TLDR
The Pufferfish framework can be used to create new privacy definitions that are customized to the needs of a given application, and it is shown how to apply it to protect unbounded continuous attributes and aggregate information.
Blowfish privacy: tuning privacy-utility trade-offs using policies
TLDR
Blowfish, a class of privacy definitions inspired by the Pufferfish framework, is presented that allows data publishers to extend differential privacy using a policy, which specifies secrets, or information that must be kept secret, and constraints that may be known about the data.
Principled Evaluation of Differentially Private Algorithms using DPBench
Differential privacy has become the dominant standard in the research community for strong privacy protection. There has been a flood of research into query answering algorithms that meet this
DPT: Differentially Private Trajectory Synthesis Using Hierarchical Reference Systems
TLDR
This paper presents DPT, a system to synthesize mobility data based on raw GPS trajectories of individuals while ensuring strong privacy protection in the form of e-differential privacy, the first system that provides an end-to-end solution.
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