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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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. This paper considers the problem of secure data aggregation (mainly summation) in a distributed setting, while ensuring differential privacy of the result. We study secure multiparty addition protocols using well known security schemes: Shamir’s secret sharing, perturbation-based, and various encryptions. We supplement our study with our new enhanced encryption scheme EFT, which is efficient and fault tolerant.Differential privacy of the final result is achieved by either distributed Laplace or Geometric mechanism (respectively DLPA or DGPA), while approximated differential privacy is achieved by diluted mechanisms. Distributed random noise is generated collectively by all participants, which draw random variables from one of several distributions: Gamma, Gauss, Geometric, or their diluted versions. We introduce a new distributed privacy mechanism with noise drawn from the Laplace distribution, which achieves smaller redundant noise with efficiency. We compare complexity and security characteristics of the protocols with different differential privacy mechanisms and security schemes. More importantly, we implemented all protocols and present an experimental comparison on their performance and scalability in a real distributed environment. Based on the evaluations, we identify our security scheme and Laplace DLPA as the most efficient for secure distributed data aggregation with differential privacy. 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. We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classifier updates and (2) propose a novel efficient encryption scheme named doubly-permuted homomorphic encryption (DPHE) which is tailored to sparse high-dimensional data. DPHE (i) decomposes sparse data into its constituent non-zero values and their corresponding support indices, (ii) applies homomorphic encryption only to the non-zero values, and (iii) employs double permutations on the support indices to make them secret. Our experimental evaluation on several public datasets shows that the proposed approach achieves comparable performance against state-of-the-art visual recognition methods while preserving privacy and significantly outperforms other privacy-preserving methods.