{"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\/da\/blog\/news\/google-otkryl-kod-biblioteki-dlya-konfidentsialnoj-obrabotki-dannyh","title":{"rendered":"Google har \u00e5bnet koden til biblioteket til fortrolig databehandling","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Google offentliggjorde<\/a><\/noindex> originale tekster fra biblioteket «Differential Privacy<\/a><\/noindex>» med implementeringen af metoder differential privatliv<\/a><\/noindex>, der g\u00f8r det muligt at udf\u00f8re statistiske operationer p\u00e5 datas\u00e6t med en tilstr\u00e6kkelig h\u00f8j pr\u00e6cision uden muligheden for at identificere enkeltposter i dem. Koden til biblioteket er skrevet i C++ og \u00e5bnet<\/a><\/noindex> er under Apache 2.0-licensen. <\/p>\n

Analyse ved hj\u00e6lp af metoder til differentiel privatliv giver organisationer mulighed for at udf\u00f8re analytiske udtr\u00e6k fra statistiske databaser uden at dele data og udtr\u00e6kke oplysninger om specifikke personer. For eksempel, for at identificere forskelle i patientpleje, kan forskere f\u00e5 information, der g\u00f8r det muligt at sammenligne den gennemsnitlige opholdstid for patienter p\u00e5 hospitaler, samtidig med at patienternes fortrolighed opretholdes og oplysninger om dem ikke kan udskilles.<\/p>\n

Det foresl\u00e5ede bibliotek inkluderer implementeringen af flere algoritmer til at danne aggregeret statistik baseret p\u00e5 datas\u00e6t, der indeholder fortrolige oplysninger. For at verificere korrektheden af algoritmerne er der tilg\u00e6ngelig stokastisk pr\u00f8veudtager<\/a><\/noindex>. Algoritmerne tillader at udf\u00f8re operationer som summation, t\u00e6lling, beregning af gennemsnit, middelv\u00e6rdi, varians og ordningsstatistik, herunder bestemmelse af minimum, maksimum og median. Der inkluderes ogs\u00e5 en implementering af Laplacians mekanisme<\/a><\/noindex>, som kan anvendes til beregninger, der ikke er d\u00e6kket af de foruddefinerede algoritmer.<\/p>\n

Biblioteket bruger en modul\u00e6r arkitektur, der muligg\u00f8r udvidelse af den eksisterende funktionalitet og tilf\u00f8jelse af ekstra mekanismer, aggregatfunktioner og v\u00e6rkt\u00f8jer til styring af niveauet af fortrolighed.
\nBaseret p\u00e5 biblioteket til databasesystemet PostgreSQL 11 forberedt<\/a><\/noindex> udvidelse med et s\u00e6t anonyme aggregatfunktioner, der bruger metoder til differentiel privatliv — ANON_COUNT, ANON_SUM, ANON_AVG, ANON_VAR, ANON_STDDEV og ANON_NTILE.<\/p>\n

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

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

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