NÓBREGA, T. P.; http://lattes.cnpq.br/5048923517404787; NÓBREGA, Thiago Pereira da.
Resumen:
The Entity Resolution with Privacy Guarantees (REGP) intends to integrate data
private/sensitive files from various data sources maintained by different parties. The REGP
aims to identify records (for example, people or objects) that represent
the same real-world entity in private data sources maintained by different
custodians. Due to recent laws and regulations (e.g. General Regulation of
Data Protection), PPRL approaches are increasingly required in application areas
from the real world, such as health, credit analysis, public policy evaluation and security
national. In practical scenarios, the PPRL process needs to deal with efficiency problems,
efficiency (call quality) and privacy. For example, the PPRL process needs to
run on large data sources (for example, a database containing
personal information from government cash distribution and assistance programs),
with an accurate classification of entities while protecting the privacy of
the information. In this context, this work proposes improvements in the PPRL process with the
in order to mitigate some of the bottlenecks of REGP. In particular, this work presents three
major contributions to the REGP process: i) a protocol that enables auditability
computation performed during REGP, ii) an unsupervised methodology that
leverages knowledge of public datasets to train classifier based on
in Machine Learning for the REGP, and iii) a new representation, ̧for the PPRL data
encoded/anonymized that allow the use of new neuro networks and classifiers of
deep learning in the context of PPRL. The present contributions improve several
parts of the PPRL process, aiming to make it more easily used in applications of the
real world. With the contribution presented in the thesis, we hope to facilitate several applications ̃
from the real world (e.g., medical, epidemiological, and population studies) and reduce
legal/bureaucratic efforts to access and process the data, making enforcement
of these simpler applications for businesses and governments.