Deploying secure multi-party computation for financial data analysis

In this paper we describe a secure system for jointly collecting and analyzing financial data for a consortium of ICT companies. To guarantee each participant\u27s privacy, we use secret sharing and secure multi-party computation (MPC) techniques. While MPC has been used to solve real-life problems beforehand, this is the first time where the actual MPC computation was done over the internet with computing nodes spread geographically apart. We describe the system architecture, security considerations and implementation details. We also present the user feedback analysis revealing that secure multi-party computation techniques give sufficient assurance for data donors to submit their sensitive information, and act as a critical enabling feature for privacy-preserving data mining

Turvalise ühisarvutuse rakendamine

Andmetest on kasu vaid siis kui neid saab kasutada. Eriti suur lisandväärtus tekib siis, kui ühendada andmed erinevatest allikatest. Näiteks, liites kokku maksu- ja haridusandmed, saab riik läbi viia kõrghariduse erialade tasuvusanalüüse. Sama kehtib ka erasektoris - ühendades pankade maksekohustuste andmebaasid, saab efektiivsemalt tuvastada kõrge krediidiriskiga kliente. Selline andmekogude ühendamine on aga tihti konfidentsiaalsus- või privaatsusnõuete tõttu keelatud. Õigustatult, sest suuremahulised ühendatud andmekogud on atraktiivsed sihtmärgid nii häkkeritele kui ka ametnikele ja andmebaaside administraatoritele, kes oma õigusi kuritarvitada võivad. Seda sorti rünnete vastus aitab turvalise ühisarvutuse tehnoloogia kasutamine, mis võimaldab mitmed osapoolel andmeid ühiselt analüüsida, ilma et keegi neist pääseks ligi üksikutele kirjetele. Oma esimesest rakendamisest praktikas 2008. aastal on turvalise ühisarvutuse tehnoloogia praeguseks jõudnud seisu, kus seda juurutatakse hajusates rakendustes üle interneti ning seda pakutakse ka osana teistest teenustest. Käesolevas töös keskendume turvalise ühisarvutuse praktikas rakendamise tehnilistele küsimustele. Alustuseks tutvustame esimesi selle tehnoloogia rakendusi, tuvastame veel lahendamata probleeme ning pakume töö käigus välja lahendusi. Töö põhitulemus on samm-sammuline ülevaade sellise juurutuse elutsüklist, kasutades näitena esimest turvalise ühisarvutuse abil läbi viidud suuremahulisi registriandmeid hõlmavat uuringut. Sealhulgas anname ülevaate ka mittetehnilistest toimingutest nagu lepingute sõlmimine ja Andmekaitse Inspektsiooniga suhtlemine, mis tulenevad suurte organisatsioonide kaasamisest nagu seda on riigiasutused. Tulevikku vaadates pakume välja lahenduse, mis ühendab endas födereeritud andmevahetusplatvormi ja turvalise ühisarvutuse tehnoloogiat. Konkreetse lahendusena pakume Eesti riigi andmevahetuskihi X-tee täiustamist turvalise ühisarvutuse teenusega Sharemind. Selline arhitektuur võimaldaks mitmeid olemasolevaid andmekogusid uuringuteks liita efektiivselt ja turvaliselt, ilma üksikisikute privaatsust rikkumata.Data is useful only when used. This is especially true if one is able to combine several data sets. For example, combining income and educational data, it is possible for a government to get a return of investment overview of educational investments. The same is true in private sector. Combining data sets of financial obligations of their customers, banks could issue loans with lower credit risks. However, this kind of data sharing is often forbidden as citizens and customers have their privacy expectations. Moreover, such a combined database becomes an interesting target for both hackers as well as nosy officials and administrators taking advantage of their position. Secure multi-party computation is a technology that allows several parties to collaboratively analyse data without seeing any individual values. This technology is suitable for the above mentioned scenarios protecting user privacy from both insider and outsider attacks. With first practical applications using secure multi-party computation developed in 2000s, the technology is now mature enough to be used in distributed deployments and even offered as part of a service. In this work, we present solutions for technical difficulties in deploying secure multi-party computation in real-world applications. We will first give a brief overview of the current state of the art, bring out several shortcomings and address them. The main contribution of this work is an end-to-end process description of deploying secure multi-party computation for the first large-scale registry-based statistical study on linked databases. Involving large stakeholders like government institutions introduces also some non-technical requirements like signing contracts and negotiating with the Data Protection Agency. Looking into the future, we propose to deploy secure multi-party computation technology as a service on a federated data exchange infrastructure. This allows privacy-preserving analysis to be carried out faster and more conveniently, thus promoting a more informed government

Secure multi-party computation for analytics deployed as a lightweight web application

We describe the definition, design, implementation, and deployment of a secure multi-party computation protocol and web application. The protocol and application allow groups of cooperating parties with minimal expertise and no specialized resources to compute basic statistical analytics on their collective data sets without revealing the contributions of individual participants. The application was developed specifically to support a Boston Women’s Workforce Council (BWWC) study of wage disparities within employer organizations in the Greater Boston Area. The application has been deployed successfully to support two data collection sessions (in 2015 and in 2016) to obtain data pertaining to compensation levels across genders and demographics. Our experience provides insights into the particular security and usability requirements (and tradeoffs) a successful “MPC-as-a-service” platform design and implementation must negotiate.We would like to acknowledge all the members of the Boston Women’s Workforce Council, and to thank in particular MaryRose Mazzola, Christina M. Knowles, and Katie A. Johnston, who led the efforts to organize participants and deploy the protocol as part of the 100% Talent: The Boston Women’s Compact [31], [32] data collections. We also thank the Boston University Initiative on Cities (IOC), and in particular Executive Director Katherine Lusk, who brought this potential application of secure multi-party computation to our attention. The BWWC, the IOC, and several sponsors contributed funding to complete this work. Support was also provided in part by Smart-city Cloud-based Open Platform and Ecosystem (SCOPE), an NSF Division of Industrial Innovation and Partnerships PFI:BIC project under award #1430145, and by Modular Approach to Cloud Security (MACS), an NSF CISE CNS SaTC Frontier project under award #1414119

From usability to secure computing and back again

Secure multi-party computation (MPC) allows multiple parties to jointly compute the output of a function while preserving the privacy of any individual party’s inputs to that function. As MPC protocols transition from research prototypes to realworld applications, the usability of MPC-enabled applications is increasingly critical to their successful deployment and widespread adoption. Our Web-MPC platform, designed with a focus on usability, has been deployed for privacy-preserving data aggregation initiatives with the City of Boston and the Greater Boston Chamber of Commerce. After building and deploying an initial version of the platform, we conducted a heuristic evaluation to identify usability improvements and implemented corresponding application enhancements. However, it is difficult to gauge the effectiveness of these changes within the context of real-world deployments using traditional web analytics tools without compromising the security guarantees of the platform. This work consists of two contributions that address this challenge: (1) the Web-MPC platform has been extended with the capability to collect web analytics using existing MPC protocols, and (2) as a test of this feature and a way to inform future work, this capability has been leveraged to conduct a usability study comparing the two versions ofWeb-MPC. While many efforts have focused on ways to enhance the usability of privacy-preserving technologies, this study serves as a model for using a privacy-preserving data-driven approach to evaluate and enhance the usability of privacy-preserving websites and applications deployed in realworld scenarios. Data collected in this study yields insights into the relationship between usability and security; these can help inform future implementations of MPC solutions.Published versio

Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

The Crypto-democracy and the Trustworthy

In the current architecture of the Internet, there is a strong asymmetry in terms of power between the entities that gather and process personal data (e.g., major Internet companies, telecom operators, cloud providers, ...) and the individuals from which this personal data is issued. In particular, individuals have no choice but to blindly trust that these entities will respect their privacy and protect their personal data. In this position paper, we address this issue by proposing an utopian crypto-democracy model based on existing scientific achievements from the field of cryptography. More precisely, our main objective is to show that cryptographic primitives, including in particular secure multiparty computation, offer a practical solution to protect privacy while minimizing the trust assumptions. In the crypto-democracy envisioned, individuals do not have to trust a single physical entity with their personal data but rather their data is distributed among several institutions. Together these institutions form a virtual entity called the Trustworthy that is responsible for the storage of this data but which can also compute on it (provided first that all the institutions agree on this). Finally, we also propose a realistic proof-of-concept of the Trustworthy, in which the roles of institutions are played by universities. This proof-of-concept would have an important impact in demonstrating the possibilities offered by the crypto-democracy paradigm.Comment: DPM 201