Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.

Abstract. Privacy and security considerations can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery can alleviate this problem. We present a technique that uses EM mixture modeling to perform clustering on distributed data. This method controls data sharing, preventing disclosure of individual data items or any results that can be traced to an individual site.

Bacterial infection or Demodex infestation has been reported to contribute to chronic blepharitis. The association between Demodex mites and bacterial flora in this disease remains to be elucidated. Fifty-six consecutive patients diagnosed with chronic blepharitis and 46 healthy volunteers were recruited for this study. Using specimens of three epilated eyelashes and lid margin swabs, Demodex were identified microscopically and bacteria were determined by cultures, followed by colony counting and mass spectrometry. We found 191 Demodex mites, 161 D. folliculorum and 30 D. brevis, in 45 patients. Correspondingly, 101 Demodex, 63 D. folliculorum and 38 D. brevis, were found in 21 controls (p < 0.05, both). Bacterial culture-positivity was obtained in samples (eyelashes, lid margins, or both) from 54 patients and in eyelashes from 37 controls. The total colony counts and the incidences and colony counts of Propinibacterium acnes and Staphylococcus aureus from patients’ eyelashes were significantly higher than that of the controls. Furthermore, bacterial colony counts in blepharitis patients’ eyelashes with D. folliculorum were higher than that of controls with D. folliculorum (p < 0.01). Similarly, P. acnes colonies increased significantly in patients’ eyelashes with D. folliculorum (p < 0.05). These results suggest that D. folliculorum and P. acnes have a role in the occurrence of the chronic blepharitis. Further studies are required to reveal the relationship between these two organisms in blepharitis.

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

No abstract