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.
PDA is a privacy-preserving dual-functional aggregation scheme in which, every consumer disseminates only one data and then supplier computes two statistical averages (mean and variance) of all consumers @cite_3 . The paper shows by simulations that PDA possesses low computational complexity and communication overheads. In another work, the authors introduce privacy-preserving data aggregation with fault-tolerance called PDAFT @cite_28 . If PDAFT is implemented, a strong adversary is not able to gain any information, even in the case of compromising a few servers at the supplier. Like PDA, PDAFT has relatively high communication overhead and is tenacious against many security threats. In PDAFT scheme, if some consumers or servers fail, it can still work correctly.
@cite_28 @cite_3
1531182675 1597292924 2119609004 1991871638
Privacy-preserving aggregation for smart grid communications, which precisely meets the requirement of periodically collecting users' electricity consumption while preserving privacy of each individual user, has been extensively studied in recent years. However, most of existing privacy-preserving aggregation schemes are only focused on the summation aggregation. In this paper, based on the lattice cryptographic technique, we propose a novel privacy-preserving dual-functional aggregation scheme PDA for smart grid communications. With our proposed PDA scheme, each individual user just reports one data, then multiple statistic values, that is, mean and variance, of all users can be computed by the data & control center in the smart grid, while the privacy of each individual user can still be protected. Detailed security analyses demonstrate that our proposed PDA scheme is secure and robust. In addition, extensive performance evaluations also show that our proposed PDA scheme is efficient in terms of computational and communication overheads when the number of considered users is within an acceptable range. Copyright © 2015 John Wiley & Sons, Ltd. Combining and analyzing data collected at multiple administrative locations is critical for a wide variety of applications, such as detecting malicious attacks or computing an accurate estimate of the popularity of Web sites. However, legitimate concerns about privacy often inhibit participation in collaborative data aggregation. In this paper, we design, implement, and evaluate a practical solution for privacy-preserving data aggregation (PDA) among a large number of participants. Scalability and efficiency is achieved through a "semi-centralized" architecture that divides responsibility between a proxy that obliviously blinds the client inputs and a database that aggregates values by (blinded) keywords and identifies those keywords whose values satisfy some evaluation function. Our solution leverages a novel cryptographic protocol that provably protects the privacy of both the participants and the keywords, provided that proxy and database do not collude, even if both parties may be individually malicious. Our prototype implementation can handle over a million suspect IP addresses per hour when deployed across only two quad-core servers, and its throughput scales linearly with additional computational resources. Smart grid, as the next generation of power grid featured with efficient, reliable, and flexible characteristics, has received considerable attention in recent years. However, the full flourish of smart grid is still hindered by how to efficiently and effectively tackle with its security and privacy challenges. In this paper, we propose a privacy-preserving data aggregation scheme with fault tolerance, named PDAFT, for secure smart grid communications. Specifically, PDAFT uses the homomorphic Paillier Encryption technique to encrypt sensitive user data such that the control center can obtain the aggregated data without knowing individual ones, and a strong adversary who aims to threaten user privacy can learn nothing even though he has already compromised a few servers at the control center. In addition, PDAFT also supports the fault-tolerant feature, i.e., PDAFT can still work well even when some user failures and server malfunctions occur. Through extensive analysis, we demonstrate that PDAFT not only resists various security threats and preserves user privacy, but also has significantly less communication overhead compared with those previously reported competitive approaches. Privacy-preserving metering aggregation is regarded as an important research topic in securing a smart grid. In this paper, we first identify and formalize a new attack, in which the attacker could exploit the information about the presence or absence of a specific person to infer his meter readings. This attack, coined as human-factor-aware differential aggregation (HDA) attack, cannot be addressed in existing privacy-preserving aggregation protocols proposed for smart grids. We give a formal definition on it and propose two novel protocols, including basic scheme and advanced scheme, to achieve privacy-preserving smart metering data aggregation and to resist the HDA attack. Our protocol ensures that smart meters periodically upload encrypted measurements to a (electricity) supplier aggregator such that the aggregator is able to derive the aggregated statistics of all meter measurements but is unable to learn any information about the human activities. We present the formal security analysis for the proposed protocol to guarantee the strong privacy. Moreover, we evaluate the performance of our protocol in a Java-based implementation under different parameters. The performance and utility analysis shows that our protocol is simple, efficient, and practical.