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.
In @cite_6 authors propose an algorithm for data collection with self-awareness protection. The paper considers some data aggregators and consumers in a smart grid where some of the respondents may not participate in contributing their personal data or submit erroneous data. To overcome this issue a self-awareness protocol is proposed to enhance trust of the respondents when sending their personal data to the data aggregators. In this scheme, all consumers collaborate with each other to preserve the privacy. They have hired an idea, which allows respondents to know protection level before the data submission process is initiated. The work is motivated by @cite_23 and @cite_10 . In @cite_23 , co-privacy (co-operative privacy) is introduced. Co-privacy claims that best solution to achieve privacy is to help other parties to achieve their privacy.
@cite_10 @cite_23 @cite_6
1553453440 1582127143 195135924
We introduce the novel concept of coprivacy or co-operative privacy to make privacy preservation attractive. A protocol is coprivate if the best option for a player to preserve her privacy is to help another player in preserving his privacy. Coprivacy makes an individual�s privacy preservation a goal that rationally interests other individuals: it is a matter of helping oneself by helping someone else. We formally define coprivacy in terms of Nash equilibria. We then extend the concept to: i) general coprivacy, where a helping player�s utility (i.e. interest) may include earning functionality and security in addition to privacy; ii) mixed coprivacy, where mixed strategies and mixed Nash equilibria are allowed with some restrictions; iii) correlated coprivacy, in which Nash equilibria are replaced by correlated equilibria. Coprivacy can be applied to any peer-to-peer (P2P) protocol. We illustrate coprivacy in P2P anonymous keyword search, in content privacy in social networks, in vehicular network communications and in controlled content distribution and digital oblivion enforcement. We introduce the novel concept of coprivacy or co-operative privacy to make privacy preservation attractive. A protocol is coprivate if the best option for a player to preserve her privacy is to help another player in preserving his privacy. Coprivacy makes an individual's privacy preservation a goal that rationally interests other individuals: it is a matter of helping oneself by helping someone else. We formally define coprivacy in terms of Nash equilibria. We then extend the concept to: i) general coprivacy, where a helping player's utility (i.e. interest) may include earning functionality and security in addition to privacy; ii) mixed coprivacy, where mixed strategies and mixed Nash equilibria are allowed with some restrictions; iii) correlated coprivacy, in which Nash equilibria are replaced by correlated equilibria. Coprivacy can be applied to any peer-to-peer (P2P) protocol. We illustrate coprivacy in P2P user-private information retrieval, and also in content privacy in on-line social networking. Data privacy protection is an emerging issue in data collection due to increasing concerns related to security and privacy. In the current data collection approaches, data collector is a dominant player who enforces the secure protocol. In other words, privacy protection is only defined by the data collector without the participation of any respondents. Furthermore, the privacy protection becomes more crucial when the raw data analysis is performed by the data collector itself. In view of this, some of the respondents might refuse to contribute their personal data or submit inaccurate data. In this paper, we study a self-awareness protocol to raise the confidence of the respondents when submitting their personal data to the data collector. Our self-awareness protocol requires each respondent to help others in preserving his privacy. At the end of the protocol execution, respondents can verify the protection level (i.e., k-anonymity) they will receive from the data collector.