3 research outputs found
Privacy-Preserving and Collusion-Resistant Charging Coordination Schemes for Smart Grid
Energy storage units (ESUs) including EVs and home batteries enable several
attractive features of the modern smart grids such as effective demand response
and reduced electric bills. However, uncoordinated charging of ESUs stresses
the power system. In this paper, we propose privacy-preserving and
collusion-resistant charging coordination centralized and decentralized schemes
for the smart grid. The centralized scheme is used in case of robust
communication infrastructure that connects the ESUs to the utility, while the
decentralized scheme is useful in case of infrastructure not available or
costly. In the centralized scheme, each energy storage unit should acquire
anonymous tokens from a charging controller (CC) to send multiple charging
requests to the CC via the aggregator. CC can use the charging requests to
enough data to run the charging coordination scheme, but it cannot link the
data to particular ESUs or reveal any private information. Our centralized
scheme uses a modified knapsack problem formulation technique to maximize the
amount of power delivered to the ESUs before the charging requests expire
without exceeding the available maximum charging capacity. In the decentralized
scheme, several ESUs run the scheme in a distributed way with no need to
aggregator or CC. One ESU is selected as a head node that should decrypt the
ciphertext of the aggregated messages of the ESUs' messages and broadcast it to
the community while not revealing the ESUs' individual charging demands. Then,
ESUs can coordinate charging requests based on the aggregated charging demand
while not exceeding the maximum charging capacity. Extensive experiments and
simulations are conducted to demonstrate that our schemes are efficient and
secure against various attacks, and can preserve ESU owner's privacy
EPIC: Efficient Privacy-Preserving Scheme with E2E Data Integrity and Authenticity for AMI Networks
In Advanced Metering Infrastructure (AMI) networks, smart meters should send
fine-grained power consumption readings to electric utilities to perform
real-time monitoring and energy management. However, these readings can leak
sensitive information about consumers' activities. Various privacy-preserving
schemes for collecting fine-grained readings have been proposed for AMI
networks. These schemes aggregate individual readings and send an aggregated
reading to the utility, but they extensively use asymmetric-key cryptography
which involves large computation/communication overhead. Furthermore, they do
not address End-to-End (E2E) data integrity, authenticity, and computing
electricity bills based on dynamic prices. In this paper, we propose EPIC, an
efficient and privacy-preserving data collection scheme with E2E data integrity
verification for AMI networks. Using efficient cryptographic operations, each
meter should send a masked reading to the utility such that all the masks are
canceled after aggregating all meters' masked readings, and thus the utility
can only obtain an aggregated reading to preserve consumers' privacy. The
utility can verify the aggregated reading integrity without accessing the
individual readings to preserve privacy. It can also identify the attackers and
compute electricity bills efficiently by using the fine-grained readings
without violating privacy. Furthermore, EPIC can resist collusion attacks in
which the utility colludes with a relay node to extract the meters' readings. A
formal proof, probabilistic analysis are used to evaluate the security of EPIC,
and ns-3 is used to implement EPIC and evaluate the network performance. In
addition, we compare EPIC to existing data collection schemes in terms of
overhead and security/privacy features
Privacy-Preserving and Efficient Data Collection Scheme for AMI Networks Using Deep Learning
In advanced metering infrastructure (AMI), smart meters (SMs), which are
installed at the consumer side, send fine-grained power consumption readings
periodically to the electricity utility for load monitoring and energy
management. Change and transmit (CAT) is an efficient approach to collect these
readings, where the readings are not transmitted when there is no enough change
in consumption. However, this approach causes a privacy problem that is by
analyzing the transmission pattern of an SM, sensitive information on the house
dwellers can be inferred. For instance, since the transmission pattern is
distinguishable when dwellers are on travel, attackers may analyze the pattern
to launch a presence-privacy attack (PPA) to infer whether the dwellers are
absent from home. In this paper, we propose a scheme, called "STDL", for
efficient collection of power consumption readings in AMI networks while
preserving the consumers' privacy by sending spoofing transmissions (redundant
real readings) using a deep-learning approach. We first use a clustering
technique and real power consumption readings to create a dataset for
transmission patterns using the CAT approach. Then, we train an attacker model
using deep-learning, and our evaluations indicate that the success rate of the
attacker is about 91%. Finally, we train a deep-learning-based defense model to
send spoofing transmissions efficiently to thwart the PPA. Extensive
evaluations are conducted, and the results indicate that our scheme can reduce
the attacker's success rate, to 13.52% in case he knows the defense model and
to 3.15% in case he does not know the model, while still achieving high
efficiency in terms of the number of readings that should be transmitted. Our
measurements indicate that the proposed scheme can reduce the number of
readings that should be transmitted by about 41% compared to continuously
transmitting readings.Comment: 16 pages, 11 figure