365 research outputs found
Smart Meter Privacy: A Utility-Privacy Framework
End-user privacy in smart meter measurements is a well-known challenge in the
smart grid. The solutions offered thus far have been tied to specific
technologies such as batteries or assumptions on data usage. Existing solutions
have also not quantified the loss of benefit (utility) that results from any
such privacy-preserving approach. Using tools from information theory, a new
framework is presented that abstracts both the privacy and the utility
requirements of smart meter data. This leads to a novel privacy-utility
tradeoff problem with minimal assumptions that is tractable. Specifically for a
stationary Gaussian Markov model of the electricity load, it is shown that the
optimal utility-and-privacy preserving solution requires filtering out
frequency components that are low in power, and this approach appears to
encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm.
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Techniques, Taxonomy, and Challenges of Privacy Protection in the Smart Grid
As the ease with which any data are collected and transmitted increases,
more privacy concerns arise leading to an increasing need to protect and preserve
it. Much of the recent high-profile coverage of data mishandling and public mis-
leadings about various aspects of privacy exasperates the severity. The Smart Grid
(SG) is no exception with its key characteristics aimed at supporting bi-directional
information flow between the consumer of electricity and the utility provider. What
makes the SG privacy even more challenging and intriguing is the fact that the very
success of the initiative depends on the expanded data generation, sharing, and pro-
cessing. In particular, the deployment of smart meters whereby energy consumption
information can easily be collected leads to major public hesitations about the tech-
nology. Thus, to successfully transition from the traditional Power Grid to the SG
of the future, public concerns about their privacy must be explicitly addressed and
fears must be allayed. Along these lines, this chapter introduces some of the privacy
issues and problems in the domain of the SG, develops a unique taxonomy of some
of the recently proposed privacy protecting solutions as well as some if the future
privacy challenges that must be addressed in the future.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111644/1/Uludag2015SG-privacy_book-chapter.pd
On the Impact of Side Information on Smart Meter Privacy-Preserving Methods
Smart meters (SMs) can pose privacy threats for consumers, an issue that has
received significant attention in recent years. This paper studies the impact
of Side Information (SI) on the performance of distortion-based real-time
privacy-preserving algorithms for SMs. In particular, we consider a deep
adversarial learning framework, in which the desired releaser (a recurrent
neural network) is trained by fighting against an adversary network until
convergence. To define the loss functions, two different approaches are
considered: the Causal Adversarial Learning (CAL) and the Directed Information
(DI)-based learning. The main difference between these approaches is in how the
privacy term is measured during the training process. On the one hand, the
releaser in the CAL method, by getting supervision from the actual values of
the private variables and feedback from the adversary performance, tries to
minimize the adversary log-likelihood. On the other hand, the releaser in the
DI approach completely relies on the feedback received from the adversary and
is optimized to maximize its uncertainty. The performance of these two
algorithms is evaluated empirically using real-world SMs data, considering an
attacker with access to SI (e.g., the day of the week) that tries to infer the
occupancy status from the released SMs data. The results show that, although
they perform similarly when the attacker does not exploit the SI, in general,
the CAL method is less sensitive to the inclusion of SI. However, in both
cases, privacy levels are significantly affected, particularly when multiple
sources of SI are included
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release
The explosion of data collection has raised serious privacy concerns in users
due to the possibility that sharing data may also reveal sensitive information.
The main goal of a privacy-preserving mechanism is to prevent a malicious third
party from inferring sensitive information while keeping the shared data
useful. In this paper, we study this problem in the context of time series data
and smart meters (SMs) power consumption measurements in particular. Although
Mutual Information (MI) between private and released variables has been used as
a common information-theoretic privacy measure, it fails to capture the causal
time dependencies present in the power consumption time series data. To
overcome this limitation, we introduce the Directed Information (DI) as a more
meaningful measure of privacy in the considered setting and propose a novel
loss function. The optimization is then performed using an adversarial
framework where two Recurrent Neural Networks (RNNs), referred to as the
releaser and the adversary, are trained with opposite goals. Our empirical
studies on real-world data sets from SMs measurements in the worst-case
scenario where an attacker has access to all the training data set used by the
releaser, validate the proposed method and show the existing trade-offs between
privacy and utility.Comment: to appear in IEEESmartGridComm 2019. arXiv admin note: substantial
text overlap with arXiv:1906.0642
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