16,706 research outputs found
Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy
Differential privacy is a promising privacy-preserving paradigm for
statistical query processing over sensitive data. It works by injecting random
noise into each query result, such that it is provably hard for the adversary
to infer the presence or absence of any individual record from the published
noisy results. The main objective in differentially private query processing is
to maximize the accuracy of the query results, while satisfying the privacy
guarantees. Previous work, notably \cite{LHR+10}, has suggested that with an
appropriate strategy, processing a batch of correlated queries as a whole
achieves considerably higher accuracy than answering them individually.
However, to our knowledge there is currently no practical solution to find such
a strategy for an arbitrary query batch; existing methods either return
strategies of poor quality (often worse than naive methods) or require
prohibitively expensive computations for even moderately large domains.
Motivated by this, we propose low-rank mechanism (LRM), the first practical
differentially private technique for answering batch linear queries with high
accuracy. LRM works for both exact (i.e., -) and approximate (i.e.,
(, )-) differential privacy definitions. We derive the
utility guarantees of LRM, and provide guidance on how to set the privacy
parameters given the user's utility expectation. Extensive experiments using
real data demonstrate that our proposed method consistently outperforms
state-of-the-art query processing solutions under differential privacy, by
large margins.Comment: ACM Transactions on Database Systems (ACM TODS). arXiv admin note:
text overlap with arXiv:1212.230
Private Model Compression via Knowledge Distillation
The soaring demand for intelligent mobile applications calls for deploying
powerful deep neural networks (DNNs) on mobile devices. However, the
outstanding performance of DNNs notoriously relies on increasingly complex
models, which in turn is associated with an increase in computational expense
far surpassing mobile devices' capacity. What is worse, app service providers
need to collect and utilize a large volume of users' data, which contain
sensitive information, to build the sophisticated DNN models. Directly
deploying these models on public mobile devices presents prohibitive privacy
risk. To benefit from the on-device deep learning without the capacity and
privacy concerns, we design a private model compression framework RONA.
Following the knowledge distillation paradigm, we jointly use hint learning,
distillation learning, and self learning to train a compact and fast neural
network. The knowledge distilled from the cumbersome model is adaptively
bounded and carefully perturbed to enforce differential privacy. We further
propose an elegant query sample selection method to reduce the number of
queries and control the privacy loss. A series of empirical evaluations as well
as the implementation on an Android mobile device show that RONA can not only
compress cumbersome models efficiently but also provide a strong privacy
guarantee. For example, on SVHN, when a meaningful
-differential privacy is guaranteed, the compact model trained
by RONA can obtain 20 compression ratio and 19 speed-up with
merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
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