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Article summary:

1. This article proposes a novel privacy-aware data fusion and prediction approach for the smart city industrial environment, which is based on the classic locality-sensitive hashing technique.

2. The proposed approach is designed to protect user privacy while guaranteeing accurate data analysis and prediction results after data fusion.

3. Experimental results show better prediction performances of the proposed approach compared to other competitive ones.

Article analysis:

The article is generally trustworthy and reliable, as it provides a detailed description of the proposed privacy-aware data fusion and prediction approach for the smart city industrial environment, along with experimental results that demonstrate its effectiveness compared to other competitive approaches. The article does not appear to be biased or one-sided in its reporting, as it presents both sides of the argument equally and objectively. Furthermore, all claims made in the article are supported by evidence from experiments conducted on a real-world dataset.

The only potential issue with this article is that it does not explore any counterarguments or alternative approaches to solving this problem. While this may be due to space constraints, it would have been beneficial if some counterarguments were explored in order to provide a more comprehensive overview of the topic at hand. Additionally, there is no promotional content present in the article, nor does it appear to be partial in any way. Finally, possible risks associated with using this approach are noted throughout the article, thus providing readers with an informed understanding of both its benefits and drawbacks.