As smart grids are getting popular and being widely implemented, preserving the privacy of consumers is becoming more substantial. Power generation and pricing in smart grids depends on the continuously gathered information from the consumers. However, having access to the data relevant to the electricity consumption of each individual consumer is in conflict with its privacy. One common approach for preserving privacy is to aggregate data of different consumers and to use their smart-meters for calculating the bills. But in this approach, malicious consumers who send erroneous data to take advantage or disrupt smart grid cannot be identified. In this paper, we propose a new statistical-based scheme for data gathering and billing in which the privacy of consumers is preserved, and at the same time, if any consumer with erroneous data can be detected. Our simulation results verify these matters.
DPAFT @cite_25 is another privacy-preserving data collection scheme which supports both differential privacy and fault tolerance at the same time. It is claimed that, DPAFT surpass other schemes in many aspects, such as storage cost, computation complexity, utility of differential privacy, robustness of fault tolerance, and the efficiency of consumer addition or removal @cite_25 . A new malfunctioning data aggregation scheme, named MuDA, is introduced in @cite_24 . The scheme is resistant to differential attacks and keeps consumers’ information secret with an acceptable noise rate. PDAFT @cite_24 , DPAFT @cite_25 , and MuDA @cite_24 , shows nearly same characteristics with differences on their cryptographic methods @cite_4 . PDAFT employs homomorphic Paillier cryptosystem @cite_30 , while DPAFT and MUDA use Boneh-Goh-Nissim cryptosystem @cite_33 .
@cite_30 @cite_4 @cite_33 @cite_24 @cite_25
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This paper investigates a novel computational problem, namely the Composite Residuosity Class Problem, and its applications to public-key cryptography. We propose a new trapdoor mechanism and derive from this technique three encryption schemes : a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA. Our cryptosystems, based on usual modular arithmetics, are provably secure under appropriate assumptions in the standard model. In this paper, we present a comprehensive survey of privacy-preserving schemes for Smart Grid communications. Specifically, we select and in-detail examine thirty privacy preserving schemes developed for or applied in the context of Smart Grids. Based on the communication and system models, we classify these schemes that are published between 2013 and 2016, in five categories, including, 1) Smart grid with the advanced metering infrastructure, 2) Data aggregation communications, 3) Smart grid marketing architecture, 4) Smart community of home gateways, and 5) Vehicle-to grid architecture. For each scheme, we survey the attacks of leaking privacy, countermeasures, and game theoretic approaches. In addition, we review the survey articles published in the recent years that deal with Smart Grids communications, applications, standardization, and security. Based on the current survey, several recommendations for further research are discussed at the end of this paper. Let ψ be a 2-DNF formula on boolean variables x1,...,xn ∈ 0,1 . We present a homomorphic public key encryption scheme that allows the public evaluation of ψ given an encryption of the variables x1,...,xn. In other words, given the encryption of the bits x1,...,xn, anyone can create the encryption of ψ(x1,...,xn). More generally, we can evaluate quadratic multi-variate polynomials on ciphertexts provided the resulting value falls within a small set. We present a number of applications of the system: In a database of size n, the total communication in the basic step of the Kushilevitz-Ostrovsky PIR protocol is reduced from @math to @math . An efficient election system based on homomorphic encryption where voters do not need to include non-interactive zero knowledge proofs that their ballots are valid. The election system is proved secure without random oracles but still efficient. A protocol for universally verifiable computation. Privacy-preserving data aggregation has been widely studied to meet the requirement of timely monitoring electricity consumption of users while protecting individual user’s data privacy in smart grid communications. In this paper, we propose a new multifunctional data aggregation scheme, named MuDA, for privacy-preserving smart grid communications. With MuDA, the smart grid control center can compute multiple statistical functions of users’ data in a privacy-preserving way to provide diversiform services. Moreover, MuDA is also designed to resist differential attacks that most secure data aggregation schemes may suffer. Through detailed security and utility analyses, we demonstrate that MuDA preserves users’ data privacy with acceptable noise rate. In addition, extensive performance evaluations are conducted to illustrate that our MuDA scheme is more efficient than a popular aggregation scheme in terms of communication overhead. Privacy-preserving data aggregation has been widely studied to meet the requirement of timely monitoring measurements of users while protecting individual’s privacy in smart grid communications. In this paper, a new secure data aggregation scheme, named d ifferentially p rivate data a ggregation with f ault t olerance (DPAFT), is proposed, which can achieve differential privacy and fault tolerance simultaneously. Specifically, inspired by the idea of Diffie–Hellman key exchange protocol, an artful constraint relation is constructed for data aggregation. With this novel constraint, DPAFT can support fault tolerance of malfunctioning smart meters efficiently and flexibly. In addition, DPAFT is also enhanced to resist against differential attacks, which are suffered in most of the existing data aggregation schemes. By improving the basic Boneh–Goh–Nissim cryptosystem to be more applicable to the practical scenarios, DPAFT can resist much stronger adversaries, i.e., user’s privacy can be protected in the honest-but-curious model. Extensive performance evaluations are further conducted to illustrate that DPAFT outperforms the state-of-the-art data aggregation schemes in terms of storage cost, computation complexity, utility of differential privacy, robustness of fault tolerance, and the efficiency of user addition and removal.