ARPA Confirms IEEE Shared Machine Learning Standard Enters to SA Ballot Process
We are excited to announce that the IEEE P2830 standard, targeting shared machine learning, has progressed to the ballot stage of the IEEE Standard Association (SA) Standard Development Process. The standard defines the technique framework and details of trusted execution environment-based shared machine learning, a branch of privacy-preserving computation.
Alibaba initiated the submission, which was drafted in cooperation with representatives from ARPA, Baidu, Lenovo Group, Shanghai Fudata, Zhejiang University, Megvii Technology, and the China Electronic Standardization Institute.
The IEEE (Institute of Electrical and Electronics Engineers), is the world’s largest technical professional organization, with the goal of promoting high-quality engineering, computing, and technology information around the globe. IEEE SA is the affiliate that focuses on the IEEE standards development and standards-related collaboration.
The standard P2830 defines a framework and architectures for machine learning. Specifically, it refers to the required practices for training a model using encrypted data aggregated from multiple sources and processed by a third-party trusted execution environment. The standard specifies functional components, workflows, security requirements, technical requirements, and protocols.
The ballot stage of Standard Development comes after the Standard has been drafted and once the Standards Committee has determined it is sufficiently stable. To successfully pass the ballot stage, the Standard must attain a 75% turnout from the balloting group, with 75% of the total ballots providing approval for the Standard. The working group now awaits the results.
Multiparty computation (MPC) technology, of the kind deployed by ARPA in its secure computation network, is gaining traction thanks to several high profile acquisitions and fundraising efforts. Recently, PayPal acquired Curv, a crypto custody company utilizing MPC tech for an undisclosed sum. MPC-based wallet ZenGo also recently raised $20 million in Series A funding in a round led by Insight Partners.
The focus on MPC technologies comes as part of a broader shift towards privacy-preserving technologies. Over recent months, the cryptocurrency markets have exceeded $2 trillion in market cap and the total funds invested in DeFi now exceed $100 billion. Therefore, privacy is becoming a concern for wealthier cryptocurrency investors as the large sums moved between wallets stand out and risk becoming identifiable.
ARPA has been developing and researching privacy-preserving computing techniques since 2018. The project provides a platform for private computation in industries ranging from finance to healthcare — any sector where data from multiple sources cannot be analyzed in aggregate due to regulatory, competitive, or ethical considerations. For instance, ARPA has deployed a blacklist cross-tabulation program for the financial sector, enabling organizations to conduct risk control across their shared client base, but without sharing their raw data.
Over the past two years, ARPA has been collaborating with standardization institutions and industrial partners to draft a set of privacy-preserving computation standards. By participating in the IEEE standard development, ARPA can work with global companies and academies to provide architectural and practical advice to related practitioners. This is also a great opportunity that converts ARPA’s best industrial practice into a publicly documented description.
ARPA is a blockchain-based solution for privacy-preserving computation, enabled by Multi-Party Computation (“MPC”). Founded in April 2018, the goal of ARPA is to separate data utility from ownership and enable data renting. ARPA’s MPC protocol creates ways for multiple entities to collaboratively analyze data and extract data synergies while keeping each party’s data input private and secure. ARPA allows secret sharing of private data, and the correctness of computation is verifiable using information-theoretic Message Authentication Code (MAC).
Developers can build privacy-preserving dApps on blockchains compatible with ARPA. Some immediate use cases include: credit anti-fraud, secure data wallet, precision marketing, joint AI model training, key management systems, etc. For example, banks using the ARPA network can share their credit blacklist for risk management purposes without exposing their customer data or privacy.
Team members have worked at leading institutions such as Google, Amazon, Huawei, Fosun, Tsinghua University, Fidelity Investments. ARPA is currently assisting the China Academy of Information and Communications Technology in setting the national standard for secure multi-party computation. ARPA is a corporate member of MPC Alliance and IEEE and is in partnership with fortune 500 companies to implement proofs-of-concept and MPC products. In 2019, ARPA was named the Top 10 most innovative blockchain companies in China by China Enterprise News and China Software Industry Association.
For more information about ARPA or to join our team, please contact us at firstname.lastname@example.org.
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