Sharemind: programmable secure computations with practical applications
Kuupäev
2013-01-28
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Kujutlege riigijuhti, kes soovib oma riigi ressursse mõistlikult kasutada ning hiljem teada, kas tema otsused on olnud õiged. Kõige selle jaoks peab ta koguma andmeid riigi ja selle alamate igapäevaelu kohta. Need andmed võivad sisaldada fakte inimeste eraelu (näiteks toimetuleku ja tervise) ning ettevõtete ärisaladuste kohta. Kaasaegses ühiskonnas ei tohi valitsus teada oma kodanike kohta liiga palju, sest teadmisest tulenev võim hakkab rikkuma inimeste isiklikku vabadust.
Minu doktoritöö eesmärk on lubada tundlike andmete töötlemist ilma nende omaniku konfidentsiaalsust rikkumata. Selleks kasutame turvalist ühisarvutust. Turvaline ühisarvutus on krüptograafiline meetod, millega saab digitaalsel kujul informatsioon töödelda nii, et töötleja ei näe andmeid ega oska neid omanikega siduda. Turvalise ühisarvutuse tehnoloogiat saab kasutada andmete kogumiseks, analüüsiks ja koondtulemuste avaldamiseks privaatsust säilitaval moel.
Töö tutvustab andmetöötlussüsteemi Sharemind, mis on mõeldud andmete turvaliseks töötlemiseks. Sharemind tugineb uudsetel turvalise ühisarvutuse võtetel, mis töötavad eriti hästi tänapäevaste digitaalsete arvutitega. Doktoritöö selgitab Sharemindi praktilisi turvagarantiisid ning mõõdetakse katseliselt selle jõudlust arvutitel.
Sharemindi arvutusprotokolle saab vabalt ümber järjestada. Nii saame neid kasutada selleks, et arvutada statistilisi funktsioone või käivitada keerukamaid andmete töötlemise algoritme. Doktoritöö esitleb ka uut programmeerimiskeelt nimega SecreC, mis teeb Sharemindi rakendustes kasutamise oluliselt lihtsamaks. Sharemindi abil on loodud mitmeid katserakendusi, mis näitavad kuidas seda saab kasutada privaatsust säilitava statistilise analüüsi ja andmekaeve jaoks. Lisaks katsetustele on Sharemindi abil realiseeritud ka maailma esimene praktikas kasutatav turvalise ühisarvutuse rakendus, mis kasutab andmete vahetamiseks avaliku andmesidevõrku internet. Seda rakendust on Eesti Infotehnoloogia ja Telekommunikatsiooni Liit (ITL) kasutanud oma liikmete majandusandmete analüüsiks.
Doktoritöös kirjeldatud meetodid on kasulikud nii valitsusele kui ettevõtetele, kes soovivad turvaliselt töödelda konfidentsiaalseid andmeid.
Imagine the leader of a state who wants to make wise choices on how to use the nation’s budget and also wants to know, how these decisions pay off. For this, the leader needs data from the citizens and the companies. Often, this data is private to a person (like financial status and health) or a business secret to a company. In a modern society, there are limits on how much a government can learn about its subjects before the power given by knowing too much starts to erode the freedom of the people. The goal of this work is to allow sensitive data to be processed while preserving the confidentiality of the data owner. We achieve this by using secure multiparty computation. Secure multiparty computation is a cryptographic technique that allows digital information to be processed without letting the person who is doing the processing see the values or associate them with their source. We can use this technology to collect data, analyze it and publish the aggregated result without compromising the privacy of the people. The thesis introduces Sharemind – a framework for creating secure data processing applications. Sharemind is based on new secure multiparty computation protocol suite that can be efficiently executed on current computing technology. The thesis discusses the security guarantees that Sharemind provides and measures its performance on digital computers. The secure computation protocols of Sharemind can be freely reordered to calculate many statistical functions or to evaluate more complex algorithms on the data. The thesis presents SecreC – a programming language for simplifying the use of Sharemind in applications. Sharemind has been used for building several research prototypes that demonstrate privacy preserving statistics and data mining techniques. In addition, Sharemind has been used to implement the first real-world secure multiparty computation application that worked using the public internet. The application has been used for financial reporting by the Estonian Association of Information Technology and Telecommunications. The methods described in this thesis can help both the government and companies in securely processing confidential information.
Imagine the leader of a state who wants to make wise choices on how to use the nation’s budget and also wants to know, how these decisions pay off. For this, the leader needs data from the citizens and the companies. Often, this data is private to a person (like financial status and health) or a business secret to a company. In a modern society, there are limits on how much a government can learn about its subjects before the power given by knowing too much starts to erode the freedom of the people. The goal of this work is to allow sensitive data to be processed while preserving the confidentiality of the data owner. We achieve this by using secure multiparty computation. Secure multiparty computation is a cryptographic technique that allows digital information to be processed without letting the person who is doing the processing see the values or associate them with their source. We can use this technology to collect data, analyze it and publish the aggregated result without compromising the privacy of the people. The thesis introduces Sharemind – a framework for creating secure data processing applications. Sharemind is based on new secure multiparty computation protocol suite that can be efficiently executed on current computing technology. The thesis discusses the security guarantees that Sharemind provides and measures its performance on digital computers. The secure computation protocols of Sharemind can be freely reordered to calculate many statistical functions or to evaluate more complex algorithms on the data. The thesis presents SecreC – a programming language for simplifying the use of Sharemind in applications. Sharemind has been used for building several research prototypes that demonstrate privacy preserving statistics and data mining techniques. In addition, Sharemind has been used to implement the first real-world secure multiparty computation application that worked using the public internet. The application has been used for financial reporting by the Estonian Association of Information Technology and Telecommunications. The methods described in this thesis can help both the government and companies in securely processing confidential information.
Kirjeldus
Väitekirja elektrooniline versioon ei sisalda publikatsioone.
Märksõnad
krüptograafia, konfidentsiaalne info, konfidentsiaalne info, Shareminder (tarkvara), cryptography, confidential information, multiparty computation, Shareminder (software)