Publication: Anonimización de citas médicas mediante programación con restricciones
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2016
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Abstract
El actual auge de internet y las comunicaciones genera inmensas cantidades de información. De entre toda esta información tiene especial interés aquella que es personal y privada, ya que puede comprometer la seguridad o intimidad de las personas. La información médica de una persona es una de las más vulnerables y es de vital importancia proteger la identidad de los pacientes, así como los resultados y diagnósticos a los que se someten. Este trabajo se centra en intentar dificultar la identificación de una persona a partir de datos que se publican en las bases de datos médicas de los programas de screening, generando citas que agrupen individuos con mismos datos públicos: edad, género, etc... Se propone una solución basada en la programación con restricciones y se presenta una nueva medida de anonimato. Se compara experimentalmente el incremento en el anonimato que se obtiene en nuestra propuesta con respecto a la generación aleatoria de citas.
With the rise of Internet and communications that currently exist, the amount of information generated is increasingly growing. This is especially relevant in the case of personal and private data, which can compromise the security or privacy of individuals. Medical information is one of the most vulnerable and it is vital to protect the identity of patients, and the results and diagnoses. This work focuses on trying to avoid the identify of people from data published in the medical databases of screening programs, generating appointments that group together individuals with same public data: age, gender, etc... We propose a solution based on constraint programming and a new measure of anonymity. We compare experimentally the Increased of anonymity of our proposal with respect to a random generation of appointments
With the rise of Internet and communications that currently exist, the amount of information generated is increasingly growing. This is especially relevant in the case of personal and private data, which can compromise the security or privacy of individuals. Medical information is one of the most vulnerable and it is vital to protect the identity of patients, and the results and diagnoses. This work focuses on trying to avoid the identify of people from data published in the medical databases of screening programs, generating appointments that group together individuals with same public data: age, gender, etc... We propose a solution based on constraint programming and a new measure of anonymity. We compare experimentally the Increased of anonymity of our proposal with respect to a random generation of appointments
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Máster en Ingeniería Informática, Facultad de Informática, Departamento de Sistemas Informáticos y Computación, curso 2015-2016