93 research outputs found
Privacy-friendly appliance load scheduling in smart grids
Abstract—The massive integration of renewable energy sources into the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable electrical appliances. However, communicating the users ’ needs about the usage of the electrical appliances leaks sensitive information about habits and lifestyles of the customers, thus arising privacy concerns. This paper proposes a privacy-preserving framework to allow the coordination of energy con-sumption without compromising the privacy of the users: the ser-vice requests generated by the domestic appliances are diveded in crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol based on Crowds by a set of schedulers, which schedule the requests operating directly on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained through an Integer Linear Programming formulation. I
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption aims at allowing computations on encrypted data
without decryption other than that of the final result. This could provide an
elegant solution to the issue of privacy preservation in data-based
applications, such as those using machine learning, but several open issues
hamper this plan. In this work we assess the possibility for homomorphic
encryption to fully implement its program without relying on other techniques,
such as multiparty computation (SMPC), which may be impossible in many use
cases (for instance due to the high level of communication required). We
proceed in two steps: i) on the basis of the structured program theorem
(Bohm-Jacopini theorem) we identify the relevant minimal set of operations
homomorphic encryption must be able to perform to implement any algorithm; and
ii) we analyse the possibility to solve -- and propose an implementation for --
the most fundamentally relevant issue as it emerges from our analysis, that is,
the implementation of conditionals (requiring comparison and selection/jump
operations). We show how this issue clashes with the fundamental requirements
of homomorphic encryption and could represent a drawback for its use as a
complete solution for privacy preservation in data-based applications, in
particular machine learning ones. Our approach for comparisons is novel and
entirely embedded in homomorphic encryption, while previous studies relied on
other techniques, such as SMPC, demanding high level of communication among
parties, and decryption of intermediate results from data-owners. Our protocol
is also provably safe (sharing the same safety as the homomorphic encryption
schemes), differently from other techniques such as
Order-Preserving/Revealing-Encryption (OPE/ORE).Comment: 14 pages, 1 figure, corrected typos, added introductory pedagogical
section on polynomial approximatio
Privacy-Friendly Load Scheduling of Deferrable and Interruptible Domestic Appliances in Smart Grids
The massive integration of renewable energy sources in the power grid ecosystem
with the aim of reducing carbon emissions must cope with their intrinsically
intermittent and unpredictable nature. Therefore, the grid must improve its
capability of controlling the energy demand by adapting the power consumption
curve to match the trend of green energy generation. This could be done by
scheduling the activities of deferrable and/or interruptible electrical appliances.
However, communicating the users' needs about the usage of their appliances
also leaks sensitive information about their habits and lifestyles, thus arising
privacy concerns.
This paper proposes a framework to allow the coordination of energy consumption
without compromising the privacy of the users: the service requests
generated by the domestic appliances are divided into crypto-shares using Shamir
Secret Sharing scheme and collected through an anonymous routing protocol by
a set of schedulers, which schedule the requests by directly operating on the
shares. We discuss the security guarantees provided by our proposed infrastructure
and evaluate its performance, comparing it with the optimal scheduling
obtained by means of an Integer Linear Programming formulation
A lightweight privacy-preserved spatial and temporal aggregation of energy data
Smart grid provides fine-grained real time energy consumption, and it is able to improve the efficiency of energy management. It enables the collection of energy consumption data from consumer and hence has raised serious privacy concerns. Energy consumption data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of energy con- sumption data by enabling the utility to retrieve the aggregated spatial and temporal consumption without revealing individual energy consumption. We use a lightweight cryptographic mech- anism to mask the energy consumption data by adding random noises to each energy reading and use Paillier’s additive homo- morphic encryption to protect the noises. When summing up the masked energy consumption data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of energy consumed in a neighbourhood at a particular time, or the total sum of energy consumed by a household in a day. No third party is able to derive the energy consumption pattern of a household in real time. A proof-of- concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform
- …