{"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\/novosti-interneta\/google-otkryl-kod-biblioteki-dlya-konfidentsialnoj-obrabotki-dannyh","title":{"rendered":"Google has released the code for its library for confidential data processing.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Google ha pubblicato<\/a><\/noindex> testi originali della libreria «Differential Privacy<\/a><\/noindex>» con l'implementazione dei metodi della privacy differenziale<\/a><\/noindex>, che consentono di eseguire operazioni statistiche su un insieme di dati con una precisione sufficientemente elevata senza la possibilit\u00e0 di identificare i singoli record. Il codice della libreria \u00e8 scritto in C++ e \u00e8 aperto<\/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, evitando la possibilit\u00e0 di separare i dati e isolare parametri specifici dei singoli. Ad esempio, per identificare le differenze nelle cure ai pazienti, ai ricercatori pu\u00f2 essere fornita un'informazione che consenta di confrontare il tempo medio di permanenza dei pazienti negli ospedali, mantenendo per\u00f2 la confidenzialit\u00e0 dei pazienti e impedendo l'estrazione di informazioni su di essi.<\/p>\n

La libreria proposta include l'implementazione di diversi algoritmi per la generazione di statistiche aggregate basate su insiemi di dati numerici, che includono informazioni riservate. Per verificare la correttezza del funzionamento degli algoritmi viene fornito un campione stocastico<\/a><\/noindex>. Gli algoritmi consentono di eseguire operazioni di somma, conteggio, calcolo di valori medi, deviazione standard, varianza e statistiche ordinali, inclusa la determinazione del minimo, massimo e della mediana. Fa parte anche l'implementazione del meccanismo di Laplace<\/a><\/noindex>, che pu\u00f2 essere utilizzato per calcoli non coperti dagli algoritmi predefiniti.<\/p>\n

La libreria utilizza un'architettura modulare che consente di estendere le funzionalit\u00e0 esistenti e di aggiungere meccanismi, funzioni aggregate e strumenti di gestione del livello di riservatezza.
\nBasata sulla libreria per il DBMS PostgreSQL 11 preparati<\/a><\/noindex> un'estensione con un insieme di funzioni aggregate anonime che utilizzano i 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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