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_9 , a respondent-defined privacy protection (RDPP) is introduced. It means that respondents are allowed to determine their required privacy protection level before delivering data to data collector. Unlike other methods in which data aggregators decide about the privacy protection level, in this scheme the consumers can freely define the privacy protection level.
@cite_9
195135924 2963828152 2017509273 2028990530
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. With the newly proposed privacy definition of Renyi Differential Privacy (RDP) in (Mironov, 2017), we re-examine the inherent privacy of releasing a single sample from a posterior distribution. We exploit the impact of the prior distribution in mitigating the influence of individual data points. In particular, we focus on sampling from an exponential family and specific generalized linear models, such as logistic regression. We propose novel RDP mechanisms as well as offering a new RDP analysis for an existing method in order to add value to the RDP framework. Each method is capable of achieving arbitrary RDP privacy guarantees, and we offer experimental results of their efficacy. Existing differential privacy (DP) studies mainly consider aggregation on data sets where each entry corresponds to a particular participant to be protected. In many situations, a user may pose a relational algebra query on a database with sensitive data, and desire differentially private aggregation on the result of the query. However, no existing work is able to release such aggregation when the query contains unrestricted join operations. This severely limits the applications of existing DP techniques because many data analysis tasks require unrestricted joins. One example is subgraph counting on a graph. Furthermore, existing methods for differentially private subgraph counting support only edge DP and are subject to very simple subgraphs. Until recent, whether any nontrivial graph statistics can be released with reasonable accuracy for arbitrary kind of input graphs under node DP was still an open problem. In this paper, we propose a novel differentially private mechanism that supports unrestricted joins, to release an approximation of a linear statistic of the result of some positive relational algebra calculation over a sensitive database. The error bound of the approximate answer is roughly proportional to the empirical sensitivity of the query --- a new notion that measures the maximum possible change to the query answer when a participant withdraws its data from the sensitive database. For subgraph counting, our mechanism provides a solution to achieve node DP, for any kind of subgraphs. The massive amount of sensitive survey data about individuals that agencies collect and share through the Internet is causing a great deal of privacy concerns. These concerns may discourage individuals from revealing their sensitive information. Existing data collection techniques have serious downsides in terms of both efficiency and the levels of protection they offer against various realizations of threats. Moreover, they do not provide any flexibility to the users to be able to specify acceptable levels of privacy protection before deciding whether to participate in the surveys. In this paper, we propose a two-pronged privacy protection model corresponding to these two privacy concerns: these are a new efficient anonymity preserving data collection technique and a method to incorporate heterogeneous privacy constraints. Together, they help preserve the privacy of respondents both during and after data collection.