Secure MPC for Analytics as a Web Application

Lapets, Andrei; Volgushev, Nikolaj; Bestavros, Azer; Jansen, Frederick; Varia, Mayank

doi:10.1109/secdev.2016.027

Cited by 21 publications

(12 citation statements)

References 7 publications

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“…This area was kicked off in the 1980's by Yao [493]. Thanks to both theoretical and engineering breakthroughs, the field has moved from being of a purely theoretical interest to a deployed technology in industry [78,77,295,27,191,242,243]. It is important to remark that MPC defines a set of technologies, and should be regarded more as a field, or a general notion of security in secure computation, than a technology per se.…”

Section: Secure Computationsmentioning

confidence: 99%

Advances and Open Problems in Federated Learning

Kairouz

McMahan²

2021

FNT in Machine Learning

2,233

1,214

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Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

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Section: Secure Computationsmentioning

confidence: 99%

Advances and Open Problems in Federated Learning

Kairouz

McMahan²

2021

FNT in Machine Learning

2,233

1,214

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“…It also reduces the communication and computation cost compared to the centralized system. In order to ensure the privacy of aggregated data, Lapets et al [106] suggested the use of MPC protocol over the web environment. Depending upon MPC protocol, aggregate data will be computed, while the confidentiality of the contributor's credentials is protected simultaneously.…”

Section: B Secure Multi-party Computationmentioning

confidence: 99%

Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges

et al. 2020

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Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.

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“…Web-MPC [35] leverages a variant [22] of standard additive secret sharing [10] to enable data analyses that do not require data contributors to reveal their data. This protocol allows a large number parties with limited computational resources to aggregate their data with the help of at least one party that can operate a web server.…”

Section: Web-mpc Data Aggregation Frameworkmentioning

confidence: 99%

“…The comprehensibility of the additive secret sharing protocol used by Web-MPC played a critical role in our and BWWC's ability to explain to non-technical participants that each company could contribute cumulative employee earnings to the city-wide analysis, that our institution could provide and manage the computational resources and MPC data analysis application, and that only aggregate data would be revealed by the output. Prior work discusses our experiences communicating with participating organizations about the protocol's capabilities [5,22]. Ultimately, the BWWC was able to analyze aggregate data across companies while keeping company-specific data private during three successful deployments in 2015, 2016, and 2017.…”

Section: Applications 41 100% Talent Compactmentioning

confidence: 99%

Accessible Privacy-Preserving Web-Based Data Analysis for Assessing and Addressing Economic Inequalities

Lapets

Jansen

Albab

et al. 2018

Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies

Self Cite

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An essential component of initiatives that aim to address pervasive inequalities of any kind is the ability to collect empirical evidence of both the status quo baseline and of any improvement that can be attributed to prescribed and deployed interventions. Unfortunately, two substantial barriers can arise preventing the collection and analysis of such empirical evidence: (1) the sensitive nature of the data itself and (2) a lack of technical sophistication and infrastructure available to both an initiative's beneficiaries and to those spearheading it. In the last few years, it has been shown that a cryptographic primitive called secure multi-party computation (MPC) can provide a natural technological resolution to this conundrum. MPC allows an otherwise disinterested third party to contribute its technical expertise and resources, to avoid incurring any additional liabilities itself, and (counterintuitively) to reduce the level of data exposure that existing parties must accept to achieve their data analysis goals. However, achieving these benefits requires the deliberate design of MPC tools and frameworks whose level of accessibility to non-technical users with limited infrastructure and expertise is state-of-the-art. We describe our own experiences designing, implementing, and deploying such usable web applications for secure data analysis within the context of two real-world initiatives that focus on promoting economic equality. CCS CONCEPTS • Security and privacy → Privacy-preserving protocols; Human and societal aspects of security and privacy; • Information systems → Information systems applications;

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Secure MPC for Analytics as a Web Application

Cited by 21 publications

References 7 publications

Advances and Open Problems in Federated Learning

Advances and Open Problems in Federated Learning

Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges

Accessible Privacy-Preserving Web-Based Data Analysis for Assessing and Addressing Economic Inequalities

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