316 research outputs found
MM: A general method to perform various data analysis tasks from a differentially private sketch
Differential privacy is the standard privacy definition for performing
analyses over sensitive data. Yet, its privacy budget bounds the number of
tasks an analyst can perform with reasonable accuracy, which makes it
challenging to deploy in practice. This can be alleviated by private sketching,
where the dataset is compressed into a single noisy sketch vector which can be
shared with the analysts and used to perform arbitrarily many analyses.
However, the algorithms to perform specific tasks from sketches must be
developed on a case-by-case basis, which is a major impediment to their use. In
this paper, we introduce the generic moment-to-moment (MM) method to
perform a wide range of data exploration tasks from a single private sketch.
Among other things, this method can be used to estimate empirical moments of
attributes, the covariance matrix, counting queries (including histograms), and
regression models. Our method treats the sketching mechanism as a black-box
operation, and can thus be applied to a wide variety of sketches from the
literature, widening their ranges of applications without further engineering
or privacy loss, and removing some of the technical barriers to the wider
adoption of sketches for data exploration under differential privacy. We
validate our method with data exploration tasks on artificial and real-world
data, and show that it can be used to reliably estimate statistics and train
classification models from private sketches.Comment: Published at the 18th International Workshop on Security and Trust
Management (STM 2022
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
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