A Pragmatic Introduction to Secure Multi-Party Computation

Evans, David; Kolesnikov, Vladimir; Rosulek, Mike

doi:10.1561/3300000019

Cited by 178 publications

(57 citation statements)

References 25 publications

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“…through a remote desktop connection. Infrastructures using cryptographic secure multi-party computing protocols also provide a secure setting in which data can be analyzed in an encrypted form only and only mutually calculated results can be decrypted [35] (more details will be provided in the "Results" section). However, even with such safe settings being used to perform analyses, additional efforts may need to be made to ensure that the results are also safe.…”

Section: Axis 12: Safe Settingsmentioning

confidence: 99%

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

Wirth

Meurers

Johns

et al. 2021

BMC Med Inform Decis Mak

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Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

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Section: Axis 12: Safe Settingsmentioning

confidence: 99%

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

Wirth

Meurers

Johns

et al. 2021

BMC Med Inform Decis Mak

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“…Secure multi-party computation methods, including the garbled circuits and secret sharing methods, have been actively developed for the past two decades. Readers may find more information from other sources (Lindell 2020;Evans et al 2018).…”

Section: Related Workmentioning

confidence: 99%

Confidential machine learning on untrusted platforms: a survey

Sharma

Chen

2021

Cybersecur

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With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.

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“…However, it is difficult to imagine the motivation for performing such an attack on public genome databases. Another model for data protection is the use of encrypted databases (Eykholt et al, 2017) or the use of secure multiparty computation (Evans et al, 2018). For example, access to databases does not have to be binary, allowing or denying access based on access control models.…”

Section: Security Requirements and Potential For New Approachesmentioning

confidence: 99%

Cyberbiosecurity Challenges of Pathogen Genome Databases

Vinatzer

Heath

Almohri

et al. 2019

Front. Bioeng. Biotechnol.

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Pathogen detection, identification, and tracking is shifting from non-molecular methods, DNA fingerprinting methods, and single gene methods to methods relying on whole genomes. Viral Ebola and influenza genome data are being used for real-time tracking, while food-borne bacterial pathogen outbreaks and hospital outbreaks are investigated using whole genomes in the UK, Canada, the USA and the other countries. Also, plant pathogen genomes are starting to be used to investigate plant disease epidemics such as the wheat blast outbreak in Bangladesh. While these genome-based approaches provide never-seen advantages over all previous approaches with regard to public health and biosecurity, they also come with new vulnerabilities and risks with regard to cybersecurity. The more we rely on genome databases, the more likely these databases will become targets for cyber-attacks to interfere with public health and biosecurity systems by compromising their integrity, taking them hostage, or manipulating the data they contain. Also, while there is the potential to collect pathogen genomic data from infected individuals or agricultural and food products during disease outbreaks to improve disease modeling and forecast, how to protect the privacy of individuals, growers, and retailers is another major cyberbiosecurity challenge. As data become linkable to other data sources, individuals and groups become identifiable and potential malicious activities targeting those identified become feasible. Here, we define a number of potential cybersecurity weaknesses in today's pathogen genome databases to raise awareness, and we provide potential solutions to strengthen cyberbiosecurity during the development of the next generation of pathogen genome databases.

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A Pragmatic Introduction to Secure Multi-Party Computation

Cited by 178 publications

References 25 publications

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

Confidential machine learning on untrusted platforms: a survey

Cyberbiosecurity Challenges of Pathogen Genome Databases

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