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Privacy Optimization Meets Pandemic Tracking /
Can smartphone apps help track the spread of the novel coronavirus, privately and securely? In this report, Rob Pegoraro weighs the issue of whether mobile apps can help trace and then slow the spread of COVID-19 or will end up as just another episode of botched government procurement and applicatio...
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
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O'Reilly Media, Inc.,
2020.
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| Edición: | 1st edition. |
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| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | Can smartphone apps help track the spread of the novel coronavirus, privately and securely? In this report, Rob Pegoraro weighs the issue of whether mobile apps can help trace and then slow the spread of COVID-19 or will end up as just another episode of botched government procurement and application of technology. Apple and Google have recently devised a system to track COVID-19 infections anonymously using Bluetooth with iOS and Android smartphones. This development points a spotlight on a needed debate about balancing privacy and collecting useful data. Do privacy-optimizing techniques, such as federated learning and differential privacy, offer useful alternatives to building centralized databases that may later invite abuse? This report takes a close look at this subject and then provides recommendations for software developers, public health authorities, and elected officials who want to build on the Apple-Google API. Understand the scope of the problem, including how contact tracing can help slow and stop outbreaks Take a closer look at Apple and Google's proposed remedy Learn how other countries including Singapore, India, France, and Australia have traced the spread of COVID-19 Examine the risk factors for adopting and using a decentralized system like the Apple-Google app. |
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| Descripción Física: | 1 online resource (25 pages) |