Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.

Truth Discovery from Conflicting Data: A Survey

With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery.

Truth Discovery with Memory Network

Truth discovery aims to resolve conflicts among multiple sources and find the truth. Conventional methods for truth discovery mainly investigate the mutual effect between the reliability of sources and the credibility of statements. These methods use real numbers, which have a lower representation capability than vectors to represent the reliability. In addition, neural networks have not been used for truth discovery. In this work, we propose memory-network-based models to address truth discovery. Our proposed models use feedforward and feedback memory networks to learn the representation of the credibility of statements. Specifically, our models adopt a memory mechanism to learn the reliability of sources for truth prediction. The proposed models use categorical and continuous data during model learning by automatically assigning different weights to the loss function on the basis of their own effects. Experimental results show that our proposed models outperform state-of-the-art methods for truth discovery.

Truth Discovery on Inconsistent Relational Data

In this era of big data, data are often collected from multiple sources that have different reliabilities, and there is inevitable conflict with respect to the various information obtained when it relates to the the same object.One important task is to identify the most trustworthy value out of all the conflicting claims, and this is known as truth discovery. Existing truth discovery methods simultaneously identify the most trustworthy information and source reliability degrees and are based on the idea that more reliable sources often provide more trustworthy information,and vice versa. However, there are often semantic constrains defined upon relational database, which can be violated by a single data source. To remove violations, an important task is to repair data to satisfy the constrains,and this is known as data cleaning. The two problems above may coexist, but considering them together can provide some benefits, and to the authors knowledge, this has not yet been the focus of any research. In this paper, therefore, a schema-decomposing based method is proposed to simultaneously discover the truth and to clean the data, with the aim of improving accuracy. Experimental results using real world data sets of notebooks and mobile phones, as well as simulated data sets, demonstrate the effectiveness and efficiency of our proposed method.

Cleaning of Multi-Source Uncertain Time Series Data Based on PageRank

There are errors in multi-source uncertain time series data.Truth discovery methods for time series data are effective in finding more accurate values,but some have limitations in their usability.To tackle this challenge,we propose a new and convenient truth discovery method to handle time series data.A more accurate sample is closer to the truth and,consequently,to other accurate samples.Because the mutual-confirm relationship between sensors is very similar to the mutual-quote relationship between web pages,we evaluate sensor reliability based on PageRank and then estimate the truth by sensor reliability.Therefore,this method does not rely on smoothness assumptions or prior knowledge of the data.Finally,we validate the effectiveness and efficiency of the proposed method on real-world and synthetic data sets,respectively.