For example, using differentially private machine learning to analyze the commuting patterns of individuals within a city would yield a model reflecting all the routes frequently used by a significant fraction of the population, but would not remember the commute patterns of any specific individual. In the context of machine learning, differential privacy ensures that algorithms can learn any frequent patterns in the data while preventing them from memorizing concrete details about any specific individual in the dataset. Technically speaking, differential privacy protects against membership attacks: a hypothetical adversary privy to the result of a data analysis algorithm will not be able to determine if the data of a particular individual was used in the analysis.
Differential privacy provides a framework for measuring and limiting the amount of information about individuals in a population that can be recovered from the output of a data analysis algorithm. One of the areas within the field of privacy-enhancing technologies where we are innovating on behalf of our customers is differential privacy, a well-known standard for privacy-aware data processing. At the same time, scientists and engineers are focused on continuously innovating, allowing us to bring better products and services to our customers. Making sure existing systems operate in accordance with the highest standards in data protection is the everyday job of thousands of engineers at Amazon. They also specify how such systems handle authentication inside Amazon's corporate network, effectively restricting any employee or system from accessing customer data unless such access is absolutely necessary to perform a critical business function.Ĭompliance with these policies is enforced and monitored through the entire life cycle of every system and service, from design to implementation, beta-testing, release, and run-time operations. These data handling policies specify, for example, the cryptographic requirements that any system handling customer data must satisfy, both in terms of communication and storage. In practice, this translates into a set of company-wide processes and policies that govern how every single data record is processed and stored inside Amazon's systems.īorja de Balle Pigem - machine learning scientist, Amazon Research This principle directs us to act with utmost respect for our customers’ privacy and is embedded in every aspect of how we handle customer data. At its heart, the design and implementation of every privacy-enhancing technology we use at Amazon is inspired by our relentless customer obsession. However, this answer only reflects the technical side of a much larger picture. The short answer is that we use state-of-the-art privacy-enhancing technologies. One aspect of our data-processing systems that isn't frequently shared is: how do we make sure that Amazon’s customer data is protected through the entire processes of ingestion, transportation, storage, and finally processing and modeling? Our customers benefit from these improvements in many ways: better personalization and recommendations on Amazon Fresh, Music, and Prime Video more accurate speech recognition and question answering in Alexa devices faster delivery for all our retail offerings - to name just a few.īroadly speaking, machine learning techniques help us to discover useful patterns in the data and to leverage these patterns to make better decisions on behalf of our customers. It is no secret by now that all these systems and services use machine learning techniques to constantly improve over time. We work backwards from customer needs when designing each of the products and services we proudly offer, but also when engineering all the systems and processes that power our worldwide operations. This powerful tenet drives everything we do at Amazon, every single day. Amazon strives to be the most customer-centric company on Earth.
0 kommentar(er)
