{"id":90395,"date":"2020-07-31T13:42:28","date_gmt":"2020-07-31T11:42:28","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/ibm-otkryl-tulkit-gomomorfnogo-shifrovaniya-dlya-linux"},"modified":"2020-07-31T13:42:28","modified_gmt":"2020-07-31T11:42:28","slug":"ibm-otkryl-tulkit-gomomorfnogo-shifrovaniya-dlya-linux","status":"publish","type":"post","link":"https:\/\/prohoster.info\/da\/blog\/news\/ibm-otkryl-tulkit-gomomorfnogo-shifrovaniya-dlya-linux","title":{"rendered":"IBM har \u00e5bnet et toolkit til homomorf kryptering for Linux.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

IBM har annonceret<\/a><\/noindex> om \u00e5bningen af kildekoden til v\u00e6rkt\u00f8jet FHE<\/a><\/noindex> (IBM Fully Homomorphic Encryption) med implementeringen af et system af fuld homomorfisk kryptering<\/a><\/noindex> til behandling af data i krypteret form. FHE g\u00f8r det muligt at skabe tjenester til fortrolige beregninger, hvor data behandles krypteret og ikke fremg\u00e5r i \u00e5ben form p\u00e5 noget tidspunkt. Resultatet genereres ogs\u00e5 krypteret. Koden er skrevet i C++ og distribueres<\/a><\/noindex> er under MIT-licens. Udover versionen til Linux udvikles der samtidig lignende v\u00e6rkt\u00f8jer til macOS<\/a><\/noindex> og iOS<\/a><\/noindex>, skrevet i Objective-C. En version til Android<\/a><\/noindex>.<\/p>\n

FHE underst\u00f8tter komplette<\/a><\/noindex> Homomorfe operationer, der g\u00f8r det muligt at udf\u00f8re addition og multiplikation af krypterede data (dvs. man kan udf\u00f8re vilk\u00e5rlige beregninger) og modtage et krypteret resultat, der ville v\u00e6re analogt med krypteringen af resultatet af addition eller multiplikation af de oprindelige data. Homomorf kryptering kan betragtes som n\u00e6ste trin i udviklingen af end-to-end kryptering \u2014 ud over beskyttelse af datatransmission gives der mulighed for databehandling uden at skulle dekryptere det. <\/p>\n

Fra en praktisk synsvinkel kan rammev\u00e6rket vise sig nyttigt til organiseringen af fortrolige cloud-beregninger, i elektroniske afstemningssystemer, i anonymiserede routingprotokoller, til krypteret behandling af foresp\u00f8rgsler i DBMS, til fortrolig tr\u00e6ning af maskinl\u00e6ringssystemer. Som et eksempel p\u00e5 anvendelsen af FHE n\u00e6vnes organiseringen af analysering af information om patienter i medicinske institutioner inden for forsikringsselskaber uden at forsikringsselskabet f\u00e5r adgang til oplysninger, der kunne identificere specifikke patienter. Ogs\u00e5 n\u00e6vnes<\/a><\/noindex> udvikling af maskinl\u00e6ringssystemer til at afd\u00e6kke bedrageriske aktiviteter med kreditkort baseret p\u00e5 behandling af krypterede anonyme finansielle transaktioner.<\/p>\n

V\u00e6rkt\u00f8jet inkluderer et bibliotek HElib<\/a><\/noindex> med implementering af flere skemaer for homomorfisk kryptering, et integreret udviklingsmilj\u00f8 (arbejdet udf\u00f8res via en browser) og et s\u00e6t eksempler. For at lette implementeringen er der forberedt f\u00e6rdige docker-images baseret p\u00e5 CentOS, Fedora og Ubuntu. Derudover er der tilg\u00e6ngelige instruktioner til at samle v\u00e6rkt\u00f8jet fra kildekoden og installere det p\u00e5 et lokalt system.<\/p>\n

Projektet har v\u00e6ret under udvikling siden 2009, men det er f\u00f8rst nu, at der er opn\u00e5et acceptable pr\u00e6stationsniveauer, der muligg\u00f8r praktisk anvendelse. Det bem\u00e6rkes, at FHE g\u00f8r homomorfe beregninger tilg\u00e6ngelige for alle interesserede; ved hj\u00e6lp af FHE kan almindelige virksomhedens programm\u00f8rer udf\u00f8re det samme arbejde p\u00e5 et minut, som tidligere kr\u00e6vede timer og dage med involvering af eksperter med doktorgrad.<\/p>\n


\n
\r\n \"Afspil\r\n
<\/div>\r\n <\/div><\/center><\/p>\n

Blandt andre fremskridt inden for fortrolige beregninger kan n\u00e6vnes offentligg\u00f8relsen af projektet<\/a><\/noindex> OpenDP<\/a><\/noindex> med implementeringen af metoder, der differential privatliv<\/a><\/noindex>muligg\u00f8r, at statistiske operationer over datas\u00e6t udf\u00f8res med tilstr\u00e6kkelig h\u00f8j pr\u00e6cision uden mulighed for at identificere individuelle poster i det. Projektet udvikles i f\u00e6llesskab af forskere fra Microsoft og Harvard University. Implementeringen er skrevet i Rust og Python, og leveres<\/a><\/noindex> under MIT-licensen.<\/p>\n

Analyse ved hj\u00e6lp af metoder til differentiel privatliv giver organisationer mulighed for at lave analytiske pr\u00f8ver fra statistiske databaser, uden at det er muligt at fremh\u00e6ve specifikke personers oplysninger. For eksempel, til at identificere forskelle i pleje af patienter kan forskerne f\u00e5 oplysninger, der tillader sammenligning af den gennemsnitlige opholdstid for patienter p\u00e5 hospitaler, men samtidig opretholder patienternes fortrolighed og forhindrer udtr\u00e6k af oplysninger om dem. <\/p>\n

To mekanismer anvendes til at beskytte identificerbare personlige eller fortrolige oplysninger: 1. Tilf\u00f8jelse af en lille statistisk 'st\u00f8j' til hvert resultat, som ikke p\u00e5virker n\u00f8jagtigheden af de udtrukne data, men som maskerer bidraget fra enkeltdataelementer.
\n2. Brug af et fortrolighedsbudget, der begr\u00e6nser m\u00e6ngden af dataudlevering for hver anmodning og forhindrer yderligere anmodninger, der kan kr\u00e6nke fortroligheden.<\/p>\n

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

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

\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f IBM \u043e\u0431\u044a\u044f\u0432\u0438\u043b\u0430 \u043e\u0431 \u043e\u0442\u043a\u0440\u044b\u0442\u0438\u0438 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0445 \u0442\u0435\u043a\u0441\u0442\u043e\u0432 \u0442\u0443\u043b\u043a\u0438\u0442\u0430 FHE (IBM Fully Homomorphic Encryption) \u0441 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0435\u0439 \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043f\u043e\u043b\u043d\u043e\u0433\u043e \u0433\u043e\u043c\u043e\u043c\u043e\u0440\u0444\u043d\u043e\u0433\u043e \u0448\u0438\u0444\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0434\u043b\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0448\u0438\u0444\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u043c \u0432\u0438\u0434\u0435. 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