{"id":37838,"date":"2019-10-31T22:20:06","date_gmt":"2019-10-31T19:20:06","guid":{"rendered":"https:\/\/prohoster.info\/blog\/google-otkryl-kod-biblioteki-dlya-konfidentsialnoj-obrabotki-dannyh\/"},"modified":"2019-10-31T22:20:06","modified_gmt":"2019-10-31T19:20:06","slug":"google-otkryl-kod-biblioteki-dlya-konfidentsialnoj-obrabotki-dannyh","status":"publish","type":"post","link":"https:\/\/prohoster.info\/fr\/blog\/news\/google-otkryl-kod-biblioteki-dlya-konfidentsialnoj-obrabotki-dannyh","title":{"rendered":"Google a ouvert le code de la biblioth\u00e8que pour le traitement confidentiel des donn\u00e9es","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

La soci\u00e9t\u00e9 Google a publi\u00e9<\/a><\/noindex> textes sources de la biblioth\u00e8que «Confidentialit\u00e9 diff\u00e9rentielle<\/a><\/noindex>» avec la mise en \u0153uvre des m\u00e9thodes de confidentialit\u00e9 diff\u00e9rentielle<\/a><\/noindex>, permettant d'effectuer des op\u00e9rations statistiques sur un ensemble de donn\u00e9es avec une pr\u00e9cision relativement \u00e9lev\u00e9e sans pouvoir identifier les enregistrements individuels. Le code de la biblioth\u00e8que est \u00e9crit en C++ et est ouvert<\/a><\/noindex> sous licence Apache 2.0. <\/p>\n

L'analyse utilisant des m\u00e9thodes de confidentialit\u00e9 diff\u00e9rentielle permet aux organisations de produire des \u00e9chantillons analytiques \u00e0 partir de bases de donn\u00e9es statistiques, sans pouvoir s\u00e9parer les donn\u00e9es et extraire des informations sur des personnes sp\u00e9cifiques. Par exemple, pour identifier les diff\u00e9rences dans les soins aux patients, les chercheurs peuvent se voir fournir des informations permettant de comparer la dur\u00e9e moyenne de s\u00e9jour des patients \u00e0 l'h\u00f4pital, tout en pr\u00e9servant la confidentialit\u00e9 des patients et en \u00e9vitant d'extraire des informations les concernant.<\/p>\n

La biblioth\u00e8que propos\u00e9e inclut la mise en \u0153uvre de plusieurs algorithmes pour la formation de statistiques agr\u00e9g\u00e9es \u00e0 partir d'ensembles de donn\u00e9es num\u00e9riques contenant des informations confidentielles. Pour v\u00e9rifier la validit\u00e9 des algorithmes, un \u00e9chantillonneur stochastique<\/a><\/noindex>est fourni. Les algorithmes permettent d'effectuer des op\u00e9rations de somme, de comptage, de calcul des moyennes, des \u00e9carts-types, des variances et des statistiques ordinales, y compris la d\u00e9termination des minimums, maximums et m\u00e9dianes. Il y a \u00e9galement la mise en \u0153uvre d'un m\u00e9canisme de Laplace<\/a><\/noindex>, qui peut \u00eatre utilis\u00e9 pour des calculs non couverts par des algorithmes pr\u00e9 pr\u00e9d\u00e9finis.<\/p>\n

La biblioth\u00e8que utilise une architecture modulaire, permettant d'\u00e9tendre la fonctionnalit\u00e9 existante et d'ajouter des m\u00e9canismes suppl\u00e9mentaires, des fonctions agr\u00e9g\u00e9es et des moyens de contr\u00f4ler le niveau de confidentialit\u00e9.
\nSur la base de la biblioth\u00e8que pour les SGBD PostgreSQL 11 pr\u00e9par\u00e9s<\/a><\/noindex> extension avec un ensemble de fonctions d'agr\u00e9gation anonymes utilisant des m\u00e9thodes de confidentialit\u00e9 diff\u00e9rentielle — ANON_COUNT, ANON_SUM, ANON_AVG, ANON_VAR, ANON_STDDEV et ANON_NTILE.<\/p>\n

<\/a><\/noindex><\/p>\n

Source : opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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