{"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\/it\/blog\/news\/google-otkryl-kod-biblioteki-dlya-konfidentsialnoj-obrabotki-dannyh","title":{"rendered":"Google ha aperto il codice della libreria per l'elaborazione riservata dei dati","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Azienda Google ha pubblicato<\/a><\/noindex> testi originali della libreria «Differential Privacy<\/a><\/noindex>» con l'implementazione dei metodi privacy differenziale<\/a><\/noindex>, che consentono di eseguire operazioni statistiche su un insieme di dati con un'accuratezza sufficientemente elevata senza la possibilit\u00e0 di identificare singoli record al suo interno. Il codice della libreria \u00e8 scritto in linguaggio C++ e \u00e8 open source<\/a><\/noindex> sotto licenza Apache 2.0. <\/p>\n

L'analisi utilizzando metodi di privacy differenziale consente alle organizzazioni di effettuare campionamenti analitici da database statistici, senza separare i dati e isolando parametri di persone specifiche dalle informazioni generali. Ad esempio, per identificare le differenze nella cura dei pazienti, ai ricercatori pu\u00f2 essere fornita un'informazione che consente di confrontare la media del tempo di permanenza dei pazienti negli ospedali, mantenendo per\u00f2 la riservatezza dei pazienti e non permettendo l'estrazione di dati su di loro.<\/p>\n

La libreria proposta include l'implementazione di diversi algoritmi per la formazione di statistiche aggregate basate su set di dati numerici che includono informazioni riservate. Per verificare la correttezza del funzionamento degli algoritmi \u00e8 fornito un campione stocastico<\/a><\/noindex>. Gli algoritmi consentono di eseguire su dati operazioni di somma, conteggio, calcolo delle medie, deviazione standard, varianza e statistica ordinale, compresa la determinazione del minimo, massimo e della mediana. Include anche l'implementazione di meccanismi di Laplace<\/a><\/noindex>, che possono essere utilizzati per calcoli non coperti da algoritmi predefiniti.<\/p>\n

La libreria utilizza un'architettura modulare che consente di espandere le funzionalit\u00e0 esistenti e aggiungere ulteriori meccanismi, funzioni aggregate e strumenti per la gestione del livello di riservatezza.
\nSulla base della libreria per il DBMS PostgreSQL 11 preparato<\/a><\/noindex> un'estensione con un insieme di funzioni aggregate anonime che utilizzano metodi di privacy differenziale — ANON_COUNT, ANON_SUM, ANON_AVG, ANON_VAR, ANON_STDDEV e ANON_NTILE.<\/p>\n

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

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

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[…]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-37838","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Google ha reso pubblico il codice della libreria per l'elaborazione riservata dei dati | ProHoster","description":"Google ha pubblicato i testi sorgente della libreria 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