towards privacy-first cloud computing on blockchain dawn song · developers specify trust model in...
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Towards Privacy-first Cloud Computing on Blockchain
Dawn SongProfessor, UC Berkeley
Founder and CEO, Oasis Labs
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“Data is the New Oil”
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94
134
40
77
22
38
110
3 billion
56
76
145
78.8
412.2
143
0 1,000 2,000 3,000
TJX Companies, Inc (2006)
Heartland Payment Systems (2008)
RSA Security (2011)
Sony's Playstation Network (2011)
US Office of Personnel Management (2012)
Adobe (2013)
Target Stores (2013)
Yahoo (2013)
Home Depot (2014)
JP Morgan Chase (2014)
eBay (2014)
Anthem (2015)
Adult Friend Finder (2016)
Equifax (2017)
Millions
Source: csoonline.com
Biggest Data Breaches Of the 21st Century
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Most Data Is Siloed
Business & societal value of data
Business intelligence, data insights
Privacy breaches, data exfiltration
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Users Are Losing Control of Their Data
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Machine learning
Data analytics
Flexible and easy to use
libraries and tools
High performance
Diverse cloud-scale
applications from
games to machine learning
Security and privacy
Formal guarantees for
data and computation
Oasis: Privacy-first Cloud Computing on Blockchain
All with decentralized trust, without relying on any central party
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Bank A Bank B Bank C
Fraud Detector A Fraud Detector B Fraud Detector C
Banks would all benefit by combining data to train better modelCan’t do this today because:• Privacy concerns• Regulatory risk• Misaligned incentives
Motivating example: Fraud detection
Fraud Detector
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Blockchain
Fraud Detector
Smart Contract
Fraud
detection
model
Sensitive data
on workers
Sensitive
results
Poor
performance
and high cost
Bank A Bank B Bank C
Motivating example: Fraud detection
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Oasis Blockchain
Privacy-preserving Smart contract
Our Solution Properties of Our Solution
• Automatic enforcement of codified privacy requirements
• Without relying on any central party
• Scale to real-world applications including machine learning
• Easy to use for developers without privacy expertise
Oasis: Privacy-preserving Smart Contracts at Scale
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Privacy-Preserving Smart Contracts At Scale
Oasis Blockchain Platform Layer
Application Layer
2. Privacy-preserving analytics & machine learning
1. Confidentiality-Preserving smart contract execution
3. Scalable smart contract execution
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Outline
2. Privacy-preserving analytics & machine learning
1. Confidentiality-Preserving smart contract execution
3. Scalable smart contract execution
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Outline
2. Privacy-preserving analytics & machine learning
1. Confidentiality-Preserving smart contract execution
3. Scalable smart contract execution
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Confidentiality-preserving Smart Contract Execution
Smart Contractinput
Stateold Statenew
Blockchain
Proof of Correctness
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Secure computation techniques
Trusted hardware
Fully homomorphic encryption
Secure multi-party computation
Zero-knowledge proof
PerformanceSupport for
general-purpose computation
Security mechanisms
Secure hardware
Cryptography, distributed trust
Cryptography
Cryptography,local computation
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Secure Hardware
OS
Applications
SecureEnclave
Smart contract
& data
Enclave contents
Integrity Confidentiality
Remote Attestation
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Ekiden: Confidentiality-preserving Smart Contracts
•Smart contract execution using secure computation:• Secure Enclave (e.g. Intel SGX, Keystone Enclave)
•Security proof: Universal Composability
https://arxiv.org/abs/1804.05141
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Secure Enclave as a Cornerstone Security Primitive
• Strong security capabilities
• Authenticate itself (device)
• Authenticate software
• Guarantee the integrity and privacy of execution
• Platform for building new security applications
• Couldn’t be built otherwise for the same practical performance
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2016
SEV: Secure Encrypted Virtualization
- Introduced in EYPC server processor line
- Provides confidentiality but not integrity
20172014
SGX: Software Guard Extensions
Built in to all Core™ processors
(6th-generation and later)
Trusted Execution Environment
- Hardware-based isolation
- TLK: open-source stack for TEE
2015
ARM TrustZone
Hardware-based isolation
for embedded devices
2018
- Collaboration between Berkeley & MIT
- Remedies issues in previous secure hardware
- Can be publicly analyzed and verified
- Can be manufactured by any manufacturer
- First release: Fall 2018
Keystone: Open-source secure enclave
https://keystone-enclave.github.io
Trusted hardware timelineClosed source
Open source
Intel SGX version 2
- In pipeline
- Drivers already available
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Challenges in Secure Hardware
• How secure can it be? Under what threat models?
• What would you entrust with secure hardware?
• Your bitcoin keys
• Financial data
• Health data
• Can we create trustworthy secure enclave as a cornerstone security primitive?
• Widely deployed, enable secure systems on top
• A new secure computation era
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Path to Trustworthy Secure Enclave
• Open source design
• Provides transparency & enables high assurance
• Builds a community
• Formal verification
• Secure supply-chain management
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Open-source Secure Enclave
http://www.keystone-enclave.org
1st release in Fall 2018
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Keystone Enclave
● Open-source Trusted Execution Environment (TEE) based on the RISC-V ISA● Strong Memory Isolation
○ ISA-enforced memory access management○ Separate virtual memory management without relying on the OS
● Simple and Portable○ Exploits standard RISC-V ISA primitive: PMP
● Remote Attestation● Open Source
○ Full software/hardware stack will be released○ Run on many platforms: QEMU, Amazon AWS FPGA (FireSim), HiFive
Unleashed, ...
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Foundation: 100+ Members
RISC-V Foundation
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Workshop on Building Open Source Secure Enclaves
● Goal○ To bring the architecture/systems-security community together towards
building open source, secure, and performant enclaves○ Aug 29, Berkeley, https://keystone-enclave.org/workshop-website-2018/
● Participants
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Outline
2. Privacy-preserving analytics & machine learning
1. Confidentiality-Preserving smart contract execution
3. Scalable smart contract execution
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Do Neural Networks Remember Training Data?
Can Attackers Extract Secrets (in Training Data) from (Querying) Learned Models?
N Carlini, C Liu, J Kos, Ú Erlingsson, and D Song.
"The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting
Secrets". 2018.
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Extracting Social Security Number from Language Model
• Learning task: train a language model on Enron Email dataset
• Containing actual people’s credit card and social security numbers
• New attacks: can extract 3 of the 10 secrets completely by querying trained models
• New measure “Exposure” for memorization
• Used in Google Smart Compose
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Preventing Memorization
• Differential Privacy: a formal notion of privacy to protect sensitive inputs
• Solution: train a differentially-private neural network
• Exposure is lower empirically
• Attack unable to extract secrets
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Differential Privacy: a Formal Privacy Definition
• Outcome is the same with or without Joe’s data
• Holds for every user and everydatabase
• Immune to re-identification attacks
• Parameterized by ε(the privacy budget)
≈Query Result #1
Query Result #2
Database #1 Database #2
Query
+ =Joe
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Real-world Use of Differential Privacy
• Previous work on differential privacy is either:
• Theoretical
• Targeted for specialized applications
• Google: top websites visited
• Apple: top emojis used
• No previous real-world deployments of differential privacy for general-purpose analytics
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Challenges for Practical General-purpose Differential Privacy for SQL Queries
•Usability for non-experts
•Broad support for analytics queries
• Easy integration with existing data environments
No existing system addresses these issues
Collaboration with Uber: address practical deployment challenges
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Chorus: a Framework for Privacy-preserving Analytics
• Usable by non-experts
• Analyst does not need to understand differential privacy
• Chorus automatically enforces differential privacy for SQL queries
• Broad support for analytics queries
• Modular design to support wide variety of mechanisms
• Implemented mechanisms support 93% of queries in our workload
• Easy integration with existing data environments
• Chorus works with standard SQL databases
• Designed for real-world use
• Deployment underway at Uber
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Optio: Privacy-Preserving Machine Learning
• Optio provides automatic differential privacy guarantees
• Rewriting and verifying analytics and ML pipelines
• Type system to enforce privacy policies
OptioML /
Analytics
Primitives
Privacy Type
SystemComposition
Primitives
Untrusted Analyst-Specified
Shared Learning Pipelines
Distributed Execution Framework
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Real-world Deployment at Uber• Ongoing deployment for analytics
• Differential privacy
• GDPR
• Plans for public-facing systems
• Open-source release:https://github.com/uber/sql-differential-privacy
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Outline
2. Privacy-preserving analytics & machine learning
1. Confidentiality-Preserving smart contract execution
3. Scalable smart contract execution
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Scalability is More Than Just High TPS for Payment transactions
Private escrow
TokensPrivacy-preserving
machine learning
InsuranceCredit scoring
Smart contracts can have complex application logic
(e.g. ML) & may depend on each other
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Observations of Existing Approaches
Compute, Storage, Consensusare often bundled together
ComputeStorage
Consensus
1. Consensus is a slow operation
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Observations of Existing Approaches
Accounts
[A - L]
[M - Z]
ComputeStorage
Consensus
2. When sharding, coordination is expensive (e.g.
locking and atomic commit protocols)
ComputeStorage
Consensus
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Scalability for Complex Smart Contract Execution with Separation of Execution from Consensus
• New architecture for blockchain platform for scalability for complex smart contract execution
• Separation of execution from consensus
• Verifiable computing
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Verifiable computing
Techniques for verifying execution correctness:
● Majority vote and discrepancy detection● Trusted hardware (as optimization)
○ Hardware-based attestation of computation integrity● Proof systems
○ Interactive proofs○ Probabilistically checkable proofs
■ Zero-knowledge SNARKs○ Cryptographically verifiable computation
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Oasis trust model● Oasis platform does not rely on Intel SGX or secure hardware
○ Requires no more trust on any single party than Ethereum
● Integrity and scalability of Oasis platform does not depend on secure hardware○ Separating execution from consensus
○ Using verifiable computing
● Oasis platform provides a unified secure computing framework○ Developers specify trust model in their contract (e.g. if they want to use secure hardware or not)
■ Using secure hardware is only optional
○ Provides choices, flexibility for developers and users to choose best solution for each application
● Open-source secure enclave design (collaboration between Berkeley & MIT)○ Open to verification and audits by anyone, can be manufactured by any chip manufacturer
○ Provide a shared foundation for secure computing in the future
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Oasis Labs
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“
Medical data is locked in “Data Silos”.
Goal: Incentivize doctors and patients to share data and improve medical research!
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How it worksDoctors / Patients Researchers
Privacy-preserving smart contracts serve as business logic
1. Patients take picture of their medical data
2. App automatically processes data and
stores it to Oasis
3. Researchers write smart contracts and untrained models and pay for model
training
4. Receive trained model or prediction results
4. Receive payments
/
Nick Hynes, David Dao, David Yan, Raymond Cheng, Dawn Song. “A Demonstration of Sterling: A Privacy-Preserving Data Marketplace” in VLDB’18 (Demo Track)
David Dao, Dan Alistarh, Claudiu Musat, Ce Zhang. “DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation”
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Oasis: Example use cases
Private escrow Tokens Prediction market
Privacy-preserving machine learning
Regulatory compliance (enterprise)
Personalized medicine Collaborative analytics
Blind auction
Decentralized exchange
Portfolio manager Credit scoringBlockchain games
(e.g. Poker, Cryptokitties)
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Whoever Controls & Leads in AI Will Rule the World
--Nation State Leaders
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The Status Quo Today
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Who Will Be Running Our Lives?
MaximizingRevenue
MaximizingRevenue
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Misused AI for Large-scale, Automated, Targeted Manipulation
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Is there a different future?
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Intelligent Agent/Virtual Assistant under User Control
Services
Services
MaximizingUser Value
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Democratization of AI: Blockchain of Intelligent Smart Contracts
Differentially-private model
training for fraud detection
Differentially-
private fraud
detection model
Oasis Blockchain Platform
Smart contract
Fraud detection
model serving
Differentially-private model
training for medical
diagnosis & treatment
Differentially-
private medical
diagnosis &
treatment model
Smart contract
Medical diagnosis &
treatment model serving
Differentially-private financial
service recommendation model
training
Differentially-
private financial
service model
Smart contract
Financial service
recommendation model
serving
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The Start of a Journey
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Early Access
TechnicalGuidance Investor Insights Exclusive
Events
A P P L I C A T I O N S A R E N O W O P E N :
www.oasislabs.com/startup
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Fellowship
Awards
Research Areas
We are excited to announce the launch of the 2019 Oasis Fellowship to support outstanding PhD students conducting research in distributed systems, security, privacy, or machine learning.
• Distributed systems• Secure hardware• Privacy-preserving techniques• Cryptography• Machine learning and AI
• Up to $25,000 Fellowship• Collaborate with Oasis Labs researchers
Visit https://www.oasislabs.com/fellowship for more information
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Coming Soon: Oasis Devnet
Key features at time of launch:
Available: Week of November 12
Backwards
compatibility with
Ethereum
Resources
and
tutorials
Reliability and
performance
Contract KitConfidentiality
framework
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Oasis: Privacy-First Cloud Computing Platform on Blockchain
EmpowermentExperimentation Fundamental Human Rights
Advancing Frontiers
Societal Impact
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Let’s Build the Future Together
• Build an app on our testnet
www.oasislabs.com/developers
• Get involved with the Keystone project
keystone-enclave.org
• Oasis Labs is hiring!
www.oasislabs.com/team
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Find more about what we’re building at
www.oasislabs.com
Follow us on Twitter @OasisLabs