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Privacy-Preserving AI Network - PlatON 2.0

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Decentralized
Privacy-Preserving
AI Network
LatticeX

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File Servers
“Push” Pub-Sub
PIM’S
Databases
Web sites
Content Portals Search Engines
Enterprise Portals
Evolution of the W...

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Market Capilazation of the Web
1997 2020 2037
Share
of
Global
Market
Cap
0.0%
7.5%
15.0%
22.5%
30.0%
Web 1.0
Green Shoots ...

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Privacy-Preserving AI Network - PlatON 2.0

  1. 1. Decentralized Privacy-Preserving AI Network LatticeX
  2. 2. File Servers “Push” Pub-Sub PIM’S Databases Web sites Content Portals Search Engines Enterprise Portals Evolution of the Web P2P File-Sharding Groupware Marketplaces Auctions Wikis Weblogs RSS E-mail Conferencing USENET Social Networks Community Portals IM Artificial Intelligence Personal Assistants Intelligent Agents Semantic Webs Knowledge Bases Knowledge Management Ontologies Taxonomies Decentralized Ledger Privacy-Preserving Computation Knowledge Networks Degree of Social Connectivity Degree of Information Connectivity Source: Novas Pivack Enterprise Minds Group Minds Lifelogs “The Relationship Web” Decentralized Communities WEB 3.0(Semantic Web) Connects Knowledge WEB 2.0(Social Web) Connects People WEB1.0(The Web) Connects Informatiion WEB X.0(Meta Web) Connects Intelligence Semantic Weblogs Smart Marketplaces Multi Agent System PlatON The Global Brain
  3. 3. Market Capilazation of the Web 1997 2020 2037 Share of Global Market Cap 0.0% 7.5% 15.0% 22.5% 30.0% Web 1.0 Green Shoots of E-commerce Desktop Browser Access Dedecated Infrastructure $13 Trillion $2 Trillion ~$0 $20 Trillion $30 Trillion Web 2.0 Social Networks Mobile always on Cloud-driver Computation Web 3.0 AI-driver Networks Decentralized Data Architecture Privacy-preserving Computation 17% CAGP Information Technology Internet Artificial Intelligence Data Souce: ARK INVEST
  4. 4. 180 160 140 120 100 80 60 40 20 0 Size And Security of the Global Data 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 46% 25% 29% 51% 24% 25% Requires security unprotected Requires security protected Does not require security Data Source: IDC’s Data Age 2025 Study Zettabytes 33% 32% 35% 13% 42% 35%
  5. 5. Problems and Solution Challenges with the current web l Centralization: The capturing of behavioral data is locked into silos that are dominated by large players. l Privacy and security: With the increasing amount of data being captured large data centers act as honeypots for organized crime. l Scalability: With larger data sets from billions of connected devices, today’s client server model is not likely to scale for the next generation web. Problems of artificial intelligence l Centralization and De-democratisation:A handful of tech giants have monopoly control over data, algorithms, and top AI talents, AI has not been democratized. l Data abuse and privacy leakage:The centralization of AI opens the door to all sorts of abuse. Due to privacy leakage and government regulation, there is not much, and higher quality data for AI model training. l Expensive training cost: Total AI training costs continue to climb, state-of- the-art AI training model costs are likely to increase 100-fold, from roughly $1 million today to more than $100 million by 2025. AI Blockchain Cryptography
  6. 6. Let us compute https://www.platon.network What is PlatON Build an infrastructure for autonomous AI agents and their collaboration, facilitate the emergence of advanced AI, and explore the path to AGI. Make the power of AI available to anyone and make AI technology work better for the public. AI A Decentralized Privacy-Preserving AI Network Web 3.0 to WebX.0 An evolutionary path to AGI Democratizing Artificial Intelligence
  7. 7. The Privacy-Preserving AI Network A decentralized and collaborative AI network on the top layer in which AI agents collaborate at scale, accomplish complex AI services through group minds. A privacy-preserving computation overlay network on top of the underlying blockchain network, which supports AI-level complex computation, creates a decentralized AI marketplace by incentivizing more data, algorithms, and computing power to join through cryptoeconomics. The underlying trust layer that implements consensus protocols and smart contracts. PlatON's Layer1 base protocol is deeply customized for privacy-preserving AI.
  8. 8. The Stack Of Privacy-Preserving AI Network PlatON will l Focus on technologies related to privacy-preserving AI training, serving and interoperability. l Collaborate extensively with the community to create an ecosystem of AI computing and services.
  9. 9. Competitive Landscape Layer Technical Features Application Layer1 Layer2 Layer3 TPS TTF Privacy-Preserving Computation Smart Contract AI Model PlatON ✔ ✔ ✔ 10k 3s Cryptography EVM WASM ✔ Ethereum ✔ ✔ 10 6m ✗ EVM eWSM Cosmos ✔ 1k 6s ✗ ✗ EOS ✔ 4k 163s ✗ WASM Solana ✔ 50k 1.5s ✗ Rust Oasis ✔ 1k 6s TEE EVM eWSM Enigma ✔ TEE Arpa ✔ MPC Phala ✔ TEE
  10. 10. Competitive Advantages Decentralization Any user and node can connect to the network permissionless. Any data, algorithms and computing power can be securely shared, connected and traded. Anyone can develop and use artificial intelligence applications. Low training costs With blockchain and privacy-preserving computing technologies, anyone can share data and algorithms in a secure and frictionless marketplace, truly reducing marginal costs and drastically reducing training costs. Privacy-Preserving Modern cryptography-based privacy-preserving computing techniques provide a new computing paradigm that makes data and models available but not visible, allowing privacy to be fully protected and data rights to be safeguarded. Low development threshold Visualize AI model development and debugging, automated machine learning (AutoML), MLOps simplifies the whole process of managing AI models from model development, training to deployment, reducing the development threshold of AI models and improving development efficiency. High-performance High-performance asynchronous BFT consensus is achieved through optimization methods such as pipeline verification, parallel verification, and aggregated signatures, and its safety, liveness, and responsiveness are proven using formal verification methods. Regulatable and auditable All data, variables and processes used in the AI training decision making process have tamper-evident records that can be tracked and audited. The use of privacy-preserving technologies allows the use of data to satisfy regulatory regulations such as the right to be forgotten, the right to portability, conditional authorization, and minimal collection. 1 5 6 4 2 3
  11. 11. Complete Decentralized Privacy-Preserving AI stack Muti Agent System, Reinforcement Learning MLOps, AutoML, Feature Enginnering, No-code AI tools Resource scheduling, Verifiable Computation, Parallel Computing, Proxy Re-encryption PoS-BFT Consensus, Smart Contracts, Built-in Privacy- Preserving Algorithms Secure Multi-Party Computation, Federated Learning, TEE GPU-accelerated Computing, FPGA-based Hardware Acceleration Collaborative AI Network (RosettaMAS) AI Service Platform (RosettaFlow) Privacy-Preserving Computation Network (RosettaNet) Privacy-preserving AI Framework (Rosetta) TensorFlow Pytorch Spark Flink Blockchain (PlatON chain) Smart Contract On-chain privacy Consensus (Giscard) Economic Model(PPos) Hardware Acceleration(GPU/FPGA/ASIC) Privacy-Preserving AI Liblary (RosettaMLLib)
  12. 12. A Few Lines to Privacy l Compatible with Tensorflow, switch to privacy mode in 3 steps: I. Import Rosetta package II. Set MPC algorithm III. Get private input data l No knowledge of cryptography required for developers l Adapting common statistical analysis algorithms, machine learning algorithms and deep learning algorithms to privacy-preserving algorithms Privacy-Preserving AI Framework (Rosetta) Hareware Acceleration CPU GPU FPGA ASIC TensorFlow Pytorch Spark Flink Privacy-Preserving AI Lidlary (RosettaMLLib) MPC ZKP HE Federated Learning TEE Mechine Leaning Deep Leaning Statistic Network
  13. 13. Privacy-Preserving Computation on PlatON l layer1 has built-in privacy algorithms (including homomorphic encryption and zero-knowledge proof) that can be integrated into smart contracts. l Layer 2 provides secure multi-party computing protocols for privacy-preserving training of AI models, and the trained models can be deployed to Layer 3. Decetralized Resource Management Blockchain Resource Publish Resource Discovery Computing Market Data Market Algorithm Market Economic lncentive Supervision Audit Resource Scheduling Resource Location RELOAD Overlay Network DID(Decentralized ldentity) Zero-Knowlage Proof Library EVM Visual Machine WASM Visual Machine Data Provider Data Provider Data Area Computing Node Computation Area MPC Protocol Proxy Re-Encyption AI Network Secret Share Both on-chain and off-chain data privacy are supported: Computing Node Computing Node Data Node Data Node Data Node
  14. 14. AutoML Privacy-Preserving AI Service Platform Available out of the box 1. Drag-and-drop model orchestration and visual debugging 2. One-stop machine learning, end-to-end training process automation 3. MLOps pipeline automation, continuous training and deployment 4. Data visualization cockpit 5. One-click Docker deployment, support cloud and local deployment Security Compliance 1. Complete mathematical and cryptographic proofs 2. Strict authentication and authorization management 3. Compatible with both international open source and Chinese commercial secret systems 4. Blockchain depository audit 5. Secure visualization cockpit Flexible Extensions 1. Plug-in support for extended algorithms, data sources, authentication and authorization 2. support for security multi-party computation, federal learning, TEE and other privacy-preserving computation algorithm 3. Support SQL, Python and other high-level languages Feature Enginnering Model Selection Hyper-parameter Optimization Visualization Modeling Data Sources Pre-Processing Multi-party Deployment Joint Training Joint Prediction Model Monitor Data and Algorithm Marketplace Discovery Trading Evaluation Joint model Continuous Training and Deployment Privacy-preserving Computation Network
  15. 15. Application Ecosystem Marketing/Advertising User Portrait Customer Match Intelligent City Intelligent Manufacturing Intelligent Transportation Intelligent Building DeFi Model Oracle Privacy-Preserving Dex DeFi Credit Biologic Drug Screening Clinical Trials Target Discovery Finance Anti-fraud Risk Control PlatON DeFi
  16. 16. Research Team and Results World-leading research team We have established a research funding for privacy-preserving computation and have a large pool of top cryptographic talent, including professors and PhDs from major universities in China and the U.S. Research results Our research team has been conducting and publishing exploratory and in-depth research in the fields of cryptography, Internet of Things, human intelligence, as well as economics and governance. l Papers l LEAF: A Faster Secure Search Algorithm via Localization, Extraction, and Reconstruction, In ACM CCS, 2020 l Compact Zero-Knowledge Proofs for Threshold ECDSA with Trustless Setup, In PKC, 2021 l Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning, In USENIX Security, 2021 l Doubly Efficient Interactive Proofs for General Arithmetic Circuits with Linear Prover Time, In eprint 2020/1247 l zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy, In eprint 2021/673 l Open-source privacy-preserving AI framework Rosetta Research grants and project collaborations We regularly fund academic research and conferences on cryptography and privacy-preserving computation, and we collaborate deeply with various projects and research groups. l Conferences: Crypto, Eurocrypt, Asiacrypt, CCS, AsiaCCS, IDASH, etc. l Project in Ethereum: MPC implementation of Proof of Custody in Eth 2.0
  17. 17. ROADMAP 2018.05 2020.02 New Baley World Testnet 2020.10 2021.04 PlatON Mainnet 2021.10 Privacy-Preserving Dex Privacy-preserving Computing Network 2022.04 Artificial Intelligence Marketplace Forerunner Network Alaya Baley World Testnet 2022.12 Collaborative AI Network AI
  18. 18. THANK YOU Decentralized Privacy-Preserving AI Network Let us compute

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