2,225 research outputs found
Medians and Beyond: New Aggregation Techniques for Sensor Networks
Wireless sensor networks offer the potential to span and monitor large
geographical areas inexpensively. Sensors, however, have significant power
constraint (battery life), making communication very expensive. Another
important issue in the context of sensor-based information systems is that
individual sensor readings are inherently unreliable. In order to address these
two aspects, sensor database systems like TinyDB and Cougar enable in-network
data aggregation to reduce the communication cost and improve reliability. The
existing data aggregation techniques, however, are limited to relatively simple
types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose
a data aggregation scheme that significantly extends the class of queries that
can be answered using sensor networks. These queries include (approximate)
quantiles, such as the median, the most frequent data values, such as the
consensus value, a histogram of the data distribution, as well as range
queries. In our scheme, each sensor aggregates the data it has received from
other sensors into a fixed (user specified) size message. We provide strict
theoretical guarantees on the approximation quality of the queries in terms of
the message size. We evaluate the performance of our aggregation scheme by
simulation and demonstrate its accuracy, scalability and low resource
utilization for highly variable input data sets
Distributed Private Heavy Hitters
In this paper, we give efficient algorithms and lower bounds for solving the
heavy hitters problem while preserving differential privacy in the fully
distributed local model. In this model, there are n parties, each of which
possesses a single element from a universe of size N. The heavy hitters problem
is to find the identity of the most common element shared amongst the n
parties. In the local model, there is no trusted database administrator, and so
the algorithm must interact with each of the parties separately, using a
differentially private protocol. We give tight information-theoretic upper and
lower bounds on the accuracy to which this problem can be solved in the local
model (giving a separation between the local model and the more common
centralized model of privacy), as well as computationally efficient algorithms
even in the case where the data universe N may be exponentially large
MVG Mechanism: Differential Privacy under Matrix-Valued Query
Differential privacy mechanism design has traditionally been tailored for a
scalar-valued query function. Although many mechanisms such as the Laplace and
Gaussian mechanisms can be extended to a matrix-valued query function by adding
i.i.d. noise to each element of the matrix, this method is often suboptimal as
it forfeits an opportunity to exploit the structural characteristics typically
associated with matrix analysis. To address this challenge, we propose a novel
differential privacy mechanism called the Matrix-Variate Gaussian (MVG)
mechanism, which adds a matrix-valued noise drawn from a matrix-variate
Gaussian distribution, and we rigorously prove that the MVG mechanism preserves
-differential privacy. Furthermore, we introduce the concept
of directional noise made possible by the design of the MVG mechanism.
Directional noise allows the impact of the noise on the utility of the
matrix-valued query function to be moderated. Finally, we experimentally
demonstrate the performance of our mechanism using three matrix-valued queries
on three privacy-sensitive datasets. We find that the MVG mechanism notably
outperforms four previous state-of-the-art approaches, and provides comparable
utility to the non-private baseline.Comment: Appeared in CCS'1
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