Despite the ubiquity of the Internet and data today, there are several pain points in data sharing that have existed for a long time. Blockchains unleashed new capabilities of data sharing using a distributed ledger, but many of the same problems persisted — particularly privacy and security.
“As an investor, I often encountered enterprises faced with data silos, unable to use external data to solve business problems effectively,” says ArpaChain CEO, Felix Xu.
Those data silos were presented with a unique opportunity to be torn down by a new era of distributed ledgers, but Xu identifies some sticking points in that plan.
“At the same time, the data intermediaries, which control data flow, greatly increased the cost of using their data,” he says. “Data ownership and the use of the data were not clear, and such pain points have been present for a long time.”
On top of that, publicly available ledgers (i.e., blockchains) are not the ideal medium for sensitive data. Advanced cryptography can help mask user identities, and transfer amounts to an extent, but these technologies are often cumbersome and not practical for enterprise use.
Fortunately, an emerging trend in the world of cryptocurrencies is the application of secure multi-party computation (sMPC), a specialty of ArpaChain.
Reconciling Security, Privacy, and Transparency on a Blockchain
In information technology, there is often a balancing act of trade-offs between security and privacy. Blockchains threw a wrench into conventional notions of trade-offs by making publicly available ledgers that run a distributed network. As a result, they present unique problems in achieving the right balance of security and privacy.
“At their core, blockchains are a means for humans to conduct secure, verifiable, and recordable transactions online without a centralized party,“ says Xu. “The result is auditable, and the blockchain is publicly accessible, but full transparency can be problematic in the real world.”
The problem is that privacy is not a native feature of blockchains, and needs to be layered on top of or baked directly into the core protocol.
“The overarching debate boils down to this: we can share data to learn new insights that benefit society as a whole, or we can isolate data into protected silos that safeguard individual privacy,” says Xu.
Enter ArpaChain’s sMPC design — an uncompromising solution.
Without diving deep into sMPC’s, which you can find more on in ArpaChain’s whitepaper, it is a cryptographic method for computing a desired output over a large set of data among a given group of independent participants. No participant can access another’s data because, individually, they each only hold the piece of a puzzle, but collectively, they hold the puzzle.
ArpaChain deploys this concept with secret sharing and computational sharding to achieve their ARPA network product — a privacy-preserving layer two protocol for data and computational privacy on any public blockchain.
“ARPA products at present include multi-party joint risk control, supplier joint KYC, blacklist sharing, security model analysis, and other solutions,” says Xu. “Our focus is on vertical fields such as finance and transactions, to extract collaborative data value for enterprises.”
The advantageous role that ARPA can play with enterprises is crucial for several reasons.
Enterprise Applications of ArpaChain
One of the immediate benefits of ARPA’s availability to developers is the ability for them to create private dapps without needing to understand the underlying mechanism that drives sMPC. Additionally, data can be obfuscated from any party not selectively included to look at or use the data.
“We can potentially disrupt the whole data industry by eliminating trust issues and data intermediaries within finance, IoT, healthcare, retail, energy sectors, and others where data is both valuable and sensitive,” says Xu.
For example, data consumers (i.e., enterprises) that rent data to run models, such as AI/Machine Learning for genomics analysis, can rent data at reduced costs by removing the data aggregators and intermediaries that abscond with a large portion of the profit in data transactions.
Similarly, data providers with valuable and underutilized data can securely monetize their data without sacrificing privacy — such as in healthcare where data is highly personal. They can effectively share data value but not ownership, unlocking data from providers that has historically been shuttered in silos.
ARPA is already attracting some high-profile suitors too.
“At present, ARPA has reached strategic cooperation with more than 20 large enterprises at home and abroad, including large trading institutions, financial and insurance institutions, and big data marketing agencies,” details Xu. “Several of which have been converted into orders.”
ARPA is also pegged for inclusion into Binance’s recently launched Binance Chain, and the token migration will grant the platform significantly more exposure to Binance’s massive user base.
“This migration allows ARPA token holders to experience the lightning-fast speed and user-friendliness on the leading Binance DEX platform while maintaining full control and transparency over their funds,” detailed the ARPA team in the Medium announcement. “A major portion of ARPA circulating tokens will be converted from ERC-20 to BEP-2, native to Binance Chain ecosystem.”
For ARPA, their eventual goal is to expand their service beyond enterprises and into the realm of consumer data.
“Our long-term goal is private cloud computing based on personal data, using secret sharing to store fragments of personal full-dimensional data in the cloud, where advertisers and financial institutions can call and analyze the MPC at any time,” says Xu. “Function can be customized, and individuals receive a return on data leases without disclosing any privacy.”
An ambitious endeavor indeed, but one with some promising ramifications for enterprises and mainstream users. With their testnet already live, look for ARPA to remain a leader in blockchain-based sMPC evolution in the rising digital era dominated by the value of data.
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