{"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\/sv\/blog\/news\/google-otkryl-kod-biblioteki-dlya-konfidentsialnoj-obrabotki-dannyh","title":{"rendered":"Google har sl\u00e4ppt koden f\u00f6r biblioteket f\u00f6r konfidentiell databehandling.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Google publicerade<\/a><\/noindex> ursprungliga texter fr\u00e5n biblioteket «Differential Privacy<\/a><\/noindex>» med implementation av metoder differentiell sekretess<\/a><\/noindex>, som m\u00f6jligg\u00f6r att man med tillr\u00e4ckligt h\u00f6g noggrannhet kan utf\u00f6ra statistiska operationer p\u00e5 datam\u00e4ngder utan att kunna identifiera enskilda poster i dem. Bibliotekets kod \u00e4r skriven i C++ och \u00e4r \u00f6ppen<\/a><\/noindex> \u00e4r licensierad under Apache 2.0. <\/p>\n

Analys med hj\u00e4lp av metoder f\u00f6r differentiell sekretess g\u00f6r det m\u00f6jligt f\u00f6r organisationer att ta fram analytiska urval fr\u00e5n statistiska databaser, utan att dela upp data och utvinna specifika personers parametrar fr\u00e5n den totala informationen. Till exempel, f\u00f6r att identifiera skillnader i v\u00e5rd av patienter kan forskare f\u00e5 information som m\u00f6jligg\u00f6r j\u00e4mf\u00f6relse av det genomsnittliga antalet dagar patienter tillbringar p\u00e5 sjukhus, men som samtidigt skyddar patienternas konfidentialitet och f\u00f6rhindrar att information om dem framh\u00e4vs.<\/p>\n

Det f\u00f6reslagna biblioteket inkluderar implementeringen av flera algoritmer f\u00f6r att skapa aggregerad statistik baserad p\u00e5 upps\u00e4ttningar av numeriska data som inneh\u00e5ller konfidentiell information. F\u00f6r att verifiera algoritmernas korrekthet tillhandah\u00e5lls en stokastisk provtagare<\/a><\/noindex>. Algoritmerna m\u00f6jligg\u00f6r summation, r\u00e4kning, ber\u00e4kning av medelv\u00e4rden, standardavvikelser, varians och ordningsstatistik, inklusive best\u00e4mning av minimum, maximum och median. Dessutom ing\u00e5r en implementering av Laplace-mekanismen<\/a><\/noindex>, som kan anv\u00e4ndas f\u00f6r ber\u00e4kningar som inte omfattas av de f\u00f6rbest\u00e4mda algoritmerna.<\/p>\n

Biblioteket anv\u00e4nder en modul\u00e4r arkitektur som g\u00f6r det m\u00f6jligt att ut\u00f6ka den befintliga funktionaliteten och l\u00e4gga till ytterligare mekanismer, aggregerade funktioner och verktyg f\u00f6r att hantera sekretessniv\u00e5er.
\nBaserat p\u00e5 biblioteket f\u00f6r databashanteringssystemet PostgreSQL 11 f\u00f6rberedd<\/a><\/noindex> till\u00e4gg med en upps\u00e4ttning anonyma aggregatfunktioner som anv\u00e4nder metoder f\u00f6r differentierad sekretess — ANON_COUNT, ANON_SUM, ANON_AVG, ANON_VAR, ANON_STDDEV och ANON_NTILE.<\/p>\n

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

K\u00e4lla: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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