ARPA is the privacy-preserving solution for blockchain. ARPA’s secure computation network enables 1) private smart contract, 2) unprecedented privacy protection for data-at-use, 3) scalable computational sharding via Verifiable Computation.
ARPA enables numerous use cases including secure data renting/exchange, secure cloud computation, identity protection, computation outsourcing, etc. These cases are applicable to AI, banking, insurance, healthcare, IoT, government etc.
Features & Highlights
Secure Computation: Computation is carried out securely so that no participating node can learn anything more than its prescribed output.
Verifiable Computation: Computation can be audited publicly, and its correctness can be proven. Therefore, it is possible to outsource computation from the blockchain network.
Layer 2 Solution: Combining secure and verifiable computation, the heavy-lifting work of com- putation is done off-chain. Essentially, making our secure computation protocol adaptable to any existing blockchain network.
Scalability: ARPA is designed as a Layer 2 solution. Because the verification has complexity of O(1), the on-chain network will never reach its computation limit. Therefore, we can improve the computation scalability and TPS(transaction per second) of any network. The computation capacity is increased linearly to participating nodes.
Efficiency: State-of-the-art implementation of MPC protocol is used to speed-up the secure com- putation.Though this implementation, 5-6 magnitudes of speed improvement are achieved compared to Fully Homomorphic Encryption (FHE).
Availability: World’s first general purpose MPC network for secure computation. With high avail- ability and low cost, we promote data security and privacy practice that is difficult to achieve other- wise.
Token Sale & Economics
- Project website: http://www.arpachain.io/
- Project whitepaper: https://docsend.com/view/hxzc9mp
- Token sale start time: 2019-04-25 15:00 UTC
- Ticker: ARPA
- Token type: ERC20 as TGE
- Total hard cap: $8,000,000
- Total supply: 2,000,000,000 ARPA
- Initial circulating supply: 11% of total supply
- Public sale token price: 1 ARPA = $0.02
- Public sale allocation: 6% of total supply
- Private sale token price: 1 ARPA = $0.02
- Private sale allocation: 20% of total supply
- Private sale vesting period: Locked for 3 months after the listing. Then release 10% monthly
- Seed sale token price: 1 ARPA = $0.02
- Seed sale allocation: 4% of total supply
- Seed sale vesting period: Locked for 3 months after the listing. 1/8 quarterly release for 2 years
- Token distribution: April 26, 2019
Token Release Schedule
Token Allocation & Release Note
- IEO Sale: 6% of total supply
- Private Sale: 20% of total supply
- Seed Sale: 4% of total supply
- Ecosystem: 5% of total supply
- Team: 20% of total supply
- Foundation: 15% of total supply
- Mining Rewards: 30% of total supply
- IEO Sale: No lock
- Ecosystem: No lock
- Private Sale: Locked for 3 months after the listing. Then release 10% monthly
- Seed Sale: Locked for 3 months after the listing. 1/8 quarterly release for 2 years
- Team: Locked for 3 months after the listing. 1/8 quarterly release for 2 years
- Foundation: Locked for 3 months after the listing. 1/8 quarterly release for 2 years
- Mining Rewards: 5% of total supply in first 3 years then 3% of total supply next 5 years
Token Utility & Use Cases
- Credit Anti-fraud
- Financial institutions can search shared blacklists or perform joint risk analysis for borrowers without disclosing each party's private information.
- Secure Risk Analytics
- Financial calculation agent can run risk analytics on client's encrypted data. Client's data is 100% invisible to agent.
- Precision Ads Display
- Advertisers can display ads to target customers for relevant products based on massive user behavior tags, without breaching user privacy.
- Profiling Engine
- Secure data matching of identities across multiple industries and organizations for well-rounded user profile.
- Data Wallet
- Users manage all personal sensitive data, define data policy and conveniently authorize to service providers in encrypted form.
- Secure Monetization
- Users can 'rent out' data to advertiser for product preference analysis, free of personal data leakage.
- Other use cases
- Data Marketplace
- Smart Diagnosis
- Key Management
- Blind Voting
- Dark Pool Trading
- Private Set Intersection
ARPA tokens will be utilized to perform the following interconnected and critical functions within the ecosystem:
Computation Cost: All participants in MPC computation are compensated with ARPA tokens for their contribution of computing power. The computation power consumed are mainly measured by the amount of triples consumed in one instance of computation.
Data and Model Usage Fee: To encourage the data providers to list high quality data resources on ARAP network, each MPC computation will be charged for data usage fee to compensate the data provider. All audiences of ARPA’s network, ranging from normal crypto audiences and network stake- holders to professional investment entities, can back public data or model following Additive Backing System. Additive Backing System broadens ARPA’s audiences and incentivizes early adopters and backers of ARPA’s data marketplace.
Stake and Security Deposit: MPC participants will use ARPA token as a form of safety deposit for launching and fulfilling computation jobs. Abortion during computation and other malicious actions result in loss of stake. This is to ensure fairness for all parties and limit the misuse of ARPA by bad actors.
Community Governance: Token holders above a specific threshold can have the right to vote for future accepted payment tokens and other proposals. The token holder are also able to participate in the arbitration of failed MPC computations and take part of the cheaters deposit.
Roadmap & Updates
- Q1 2018
- Idea Generation, Data Renting Business Case
- Q2 2018
- Initial Team Forming & Funding
- Q3 2018
- Whitepaper, Tech Notes
- Q4 2018
- MPC POC Demo, MPC Network Launch
- Q1 2019
- Testnet 1.0 (ASTRAEA) Release
- Q2 2019
- Business Case Development
- Q3 2019
- Security Enhancement, Protocol Improvements
- Q4 2019
- ARPA Mainnet Release
- Q1 2020
- Performance Optimizations
Xu Maotong, Founder & CEO, Xu graduated from the Stern School of Business in New York University with degrees in information technology and nance. He has nearly six years of experience within investment and business founding. While working for Fosun RZ (a subsidiary of Fosun International Ltd.), he was in charge of the nancial technology investments and AI big data. Additionally, he was independently responsible for the research and early-stage investment of blockchain. That has offered him profound experience in project management as well as skills within domestic and oversea technology and investment industries. Furthermore, he has worked for Sackler Family Of ce, Vertical Research Partners and other investment organizations in New York.
Chen Jiang, Founder & Technical director, Chen has done his Master in Computer Science at the University of Michigan. He has worked for Google Cloud Datastore, Cloud IAM and Arista Networks in Silicon Valley. Four years of experience in cloud services and security system development make him a valuable asset to the team. During his time with Google, he delivered outstanding work while he was in charge of authorization models of cloud services and automatic Hot Spot Detection of databases. Furthermore, he participated in the development of ATS (Automatic Test System) while he was working for Arista Networks.
Xu Yemu, Founder & CGO, Xu Yemu obtained his degrees in Mathematics, Actuary and Risk Management at the University He brings four years of experience in business funding and enterprise gro has worked for Fidelity Investments as a consultant, created and conducte al strategies for Apple, AT&T, HP, and other world’s top 100 enterprises. One of his biggest achievments was establishing the biggest global entrepreneur community of the midwest, Build312. Furthermore, he worked for Boro, an a ntech startup.
Zhang Lei, Founder & Chief Scientist, Zhang started programming since age 7, beginning with writing down codes on a piece of paper. Languages include C++, GO, VBA, Java, Matlab, R, Python, C , Objective C, Node JS, Java script. B.S. Zhejiang University, M.S. George Washington University. 10 years of experience at Stardust, CircleUp, AIG, World Bank as AI Data Scientist and Quant Developer. Developed the world’s rst angel investment robot at CircleUp. Cofounder and full stack at a food recommendation app in NYC. Expert at CV, recommendation system. Stress test at the FED, developed core risk management system using Blended Copula at AIG, developed infra system for ML (pipeline system) at Stardust.
Su Guantong, Researcher of cryptology, Su received his Ph.D. in crypto chips at the Tsinghua University and has studied cryptology for more than 6 years. He brings research experience in lattice-based cryptology, secure multi-party computation, homomorphic encryption, and other public key cryptographic solutions. Additionally, he has participated in projects of lattice-based cryptology and hardware security in the state key laboratory of cryptology and in the trusted computation laboratory of the Chinese University of Hong Kong. He also worked for Deephi as algorithm engineer.
Wu Yifei, Senior engineer, Wu obtained his Ph.D. of science from the University of Tokyo and his double bachelors in science and mathematics from Peking University. He has published many essays in top journals during his Ph.D.’s study, and has four years of experience in blockchain development. He became the Blockchain Tech Lead of LOTS, a Singapore based blockchain nancing platform, after his stay at Wanxiang Blockchain Labs. Besides, he has given enterprise blockchain training lectures. And he has, as the team leader, won the 1st place at 3rd SV Insight white hat hackathon.
Shen Bomo, Senior engineer, Shen graduated from the University of Iowa, majoring in Computer Science and bringing ve years of experience in system development. He was responsible for the architecture design of high-concurrence and low-latency trading systems of Wells Fargo’s investment department. He worked for Blackstone as an engineering adviser, as well as for AQR, Paulson, Moore Capital and other top hedge-fund trading systems.
Chen Shaolong, Full stack engineer, Chen owns a master in Embedded Vision from Polytech Orleans. He brings three years of experience in developing high-performance service layers. Chen participated in the industry-level VR project of Schneider and worked as a lab assistant in the CSIC laboratory of Huawei’s Paris branch. He was focusing on the development and deployment of an automatic recognition system, which is a service-level application for the optics industry.
Han Chuang, System engineer, Han has a master in Computer Science from the Carnegie Mellon University and has six year erience in the development of system levels and back end, Han has wo Google, Uber, and Amazon and has participated in the development of Google Assistant and Amazon Music. Furthermore, he led the development of the billing system for Uber drivers.
He Qinming, Researcher, Department chair of computer science, professor and doctoral supervisor in Zhejiang University; Director of Computer system structure and network security institute; Winner of the second prize of National Teaching Achievement, second prize of Science and Technology Progress in Zhejiang province as well as rst and second prizes of Teaching Achievement in Zhejiang province.
Xiao Bin, Researcher, Professor and doctoral supervisor in School of Computing in Hong Kong Polytechnic University; Editor of Journal of Parallel and Distributed Computing (JPDC); Senior member of IEEE; Member of ACM; Researcher of secure computation, security of smart contract, underlying architecture of blockchain and others.
Panos Ipeirotis, Researcher, Professor of information science in New York University; Winner of CRT Foundation Lagrange Prize in 2015; Awarded as the best writer of ISS/INFORMS essays for nine times; Winner of CAREER prize offered by NSF; Senior member of IEEE; Researcher of machine learning, data mining, and others.
Ben Gorlick, Researcher, Doctor of computer science in the University of Alaska Anchorage; Ex-product director of Blockstream, leading enterprise in the blockchain industry; Core member of Lightning Network; Co-founder of Convergence Venture Capital.
Mark Simken, Researcher, Doctor of cryptology in Aarhus University in Denmark; IBM Watson researcher focusing on effective secure computation, Zero-Knowledge Proof and ORAM.
Dragos Rotaru, Researcher, Doctor of Cryptology in Catholic University of Leuven in Belgium; COSIC researcher focusing on theoretical cryptographic protocol, applying cryptology and large-scale secure computation.
Dong Lei, Researcher, Double bachelor and doctor in Tsinghua University; Visiting Scholar at Harvard University; Ex-worker of Baidu AI Lab; Postdoctoral of MIT Senseable City Lab; Data scientist of deep learning.
Xiaoyao Li, Advisor, Doctor of machine vision in the University of Delaware; Expert in Google AI Lab deep learning.
Zhang Qingxia, Advisor, Ex-general manager of integrating technology department of global technology service division of IBM’s Great China branch; Business director of Microsoft’s Chinese branch; Chinese branch GM of Oracle Corporation.
Michael Arrington, Advisor, Co-founder of TechCrunch and Arrington XRP Capital
Li Rundong, Advisor, Doctor of Physics at Stanford University; Vice-general manager of Everbright Financial Holding Asset Management Co., Ltd; JP Morgan’s ex-expert in quanti cation algorithm for nancial derivatives.
Yu Jia’ao, Advisor, Chief Government Of cer of Sangfor.
Richard Wang, Fannie Mae’s senior engineer in big data algorithm and nancial derivative system; Director of TCFA, the biggest ethnic Chinese nancial organization in America.
Fei Ding’an, Advisor, Founder of Ledger Capital and ex-executive director of Warburg Pincus.
Ajay Lakhotia, Advisor, Ex-managing director at FOSUN Group, founder and investment director of Vertex Ventures India, board observer of IDG Ventures India and Yatra.
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