Поиск по каталогу |
(строгое соответствие)
|
- Профессиональная
- Научно-популярная
- Художественная
- Публицистика
- Детская
- Искусство
- Хобби, семья, дом
- Спорт
- Путеводители
- Блокноты, тетради, открытки
Privacy in Context: The Costs and Benefits of a New Encryption Method.
В наличии
Местонахождение: Алматы | Состояние экземпляра: новый |
Бумажная
версия
версия
Автор: Stan Trepetin
ISBN: 9783659928017
Год издания: 2016
Формат книги: 60×90/16 (145×215 мм)
Количество страниц: 228
Издательство: LAP LAMBERT Academic Publishing
Цена: 43696 тг
Положить в корзину
Способы доставки в город Алматы * комплектация (срок до отгрузки) не более 2 рабочих дней |
Самовывоз из города Алматы (пункты самовывоза партнёра CDEK) |
Курьерская доставка CDEK из города Москва |
Доставка Почтой России из города Москва |
Аннотация: The American public continues to be concerned about medical privacy. Health organizations need personally identifiable data to make care decisions; yet identifiable data are often the basis of information abuse. This book shows how de-identified data may be used for important healthcare operations. A technology adoption model is constructed to explore if a for-profit health insurer could use de-identified data. A close data analysis finds support for adding privacy protections to the insurer’s quality-of-care applications. A cost-benefit model is constructed describing the Predictive Modeling application (PMA), used to identify the insurer’s chronically-ill policy-holders. The model quantifies the decline in policy-holder care and rise in the insurer’s claim costs as the PMA must work with suboptimal data due to policy-holders' quality-of-care privacy concerns. A new encryption approach to link records despite linkage variable errors is constructed. It’s tested as part of a general data de-identification methodology--and an actual PMA’s performance is found to be the same as if executing on identifiable data. That is, key medical applications can be run on de-identified data.
Ключевые слова: Encryption, Privacy, Record Linkage, privacy-preserving data mining, de-identification, cost-benefit model, quantifying security, medical privacy