1,568 research outputs found
ARPA Whitepaper
We propose a secure computation solution for blockchain networks. The
correctness of computation is verifiable even under malicious majority
condition using information-theoretic Message Authentication Code (MAC), and
the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty
computation protocol and a layer2 solution, our privacy-preserving computation
guarantees data security on blockchain, cryptographically, while reducing the
heavy-lifting computation job to a few nodes. This breakthrough has several
implications on the future of decentralized networks. First, secure computation
can be used to support Private Smart Contracts, where consensus is reached
without exposing the information in the public contract. Second, it enables
data to be shared and used in trustless network, without disclosing the raw
data during data-at-use, where data ownership and data usage is safely
separated. Last but not least, computation and verification processes are
separated, which can be perceived as computational sharding, this effectively
makes the transaction processing speed linear to the number of participating
nodes. Our objective is to deploy our secure computation network as an layer2
solution to any blockchain system. Smart Contracts\cite{smartcontract} will be
used as bridge to link the blockchain and computation networks. Additionally,
they will be used as verifier to ensure that outsourced computation is
completed correctly. In order to achieve this, we first develop a general MPC
network with advanced features, such as: 1) Secure Computation, 2) Off-chain
Computation, 3) Verifiable Computation, and 4)Support dApps' needs like
privacy-preserving data exchange
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Performing machine learning (ML) computation on private data while
maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an
emergent field of research. Recently, PPML has seen a visible shift towards the
adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy
computation that it entails. In the SOC paradigm, computation is outsourced to
a set of powerful and specially equipped servers that provide service on a
pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for
a range of ML algorithms in SOC setting, that guarantees output delivery to the
users irrespective of any adversarial behaviour. Robustness, a highly desirable
feature, evokes user participation without the fear of denial of service.
At the heart of our framework lies a highly-efficient, maliciously-secure,
three-party computation (3PC) over rings that provides guaranteed output
delivery (GOD) in the honest-majority setting. To the best of our knowledge,
SWIFT is the first robust and efficient PPML framework in the 3PC setting.
SWIFT is as fast as (and is strictly better in some cases than) the best-known
3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We
extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as
fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20)
and twice faster than the best-known robust 4PC framework FLASH (Byali et al.
PETS'20).
We demonstrate our framework's practical relevance by benchmarking popular ML
algorithms such as Logistic Regression and deep Neural Networks such as VGG16
and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results
testify to our claims that we provide improved security guarantee while
incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear
in USENIX Security 202
uMine: A Blockchain Based on Human Miners
Blockchain technology like Bitcoin is a rapidly growing field of research which has found a wide array of applications. However, the power consumption of the mining process in the Bitcoin blockchain alone is estimated to be at least as high as the electricity consumption of Ireland which constitutes a serious liability to the widespread adoption of blockchain technology.
We propose a novel instantiation of a proof of human-work which is a cryptographic proof that an amount of human work has been exercised, and show its use in the mining process of a blockchain. Next to our instantiation there is only one other instantiation known which relies on indistinguishability obfuscation, a cryptographic primitive whose existence is only conjectured.
In contrast, our construction is based on the cryptographic principle of multiparty computation (which we use in a black box manner) and thus is the first known feasible proof of human-work scheme.
Our blockchain mining algorithm called uMine, can be regarded as an alternative energy-efficient approach to mining
HERMES: Scalable, Secure, and Privacy-Enhancing Vehicle Access System
We propose HERMES, a scalable, secure, and privacy-enhancing system for users
to share and access vehicles. HERMES securely outsources operations of vehicle
access token generation to a set of untrusted servers. It builds on an earlier
proposal, namely SePCAR [1], and extends the system design for improved
efficiency and scalability. To cater to system and user needs for secure and
private computations, HERMES utilizes and combines several cryptographic
primitives with secure multiparty computation efficiently. It conceals secret
keys of vehicles and transaction details from the servers, including vehicle
booking details, access token information, and user and vehicle identities. It
also provides user accountability in case of disputes. Besides, we provide
semantic security analysis and prove that HERMES meets its security and privacy
requirements. Last but not least, we demonstrate that HERMES is efficient and,
in contrast to SePCAR, scales to a large number of users and vehicles, making
it practical for real-world deployments. We build our evaluations with two
different multiparty computation protocols: HtMAC-MiMC and CBC-MAC-AES. Our
results demonstrate that HERMES with HtMAC-MiMC requires only approx 1,83 ms
for generating an access token for a single-vehicle owner and approx 11,9 ms
for a large branch of rental companies with over a thousand vehicles. It
handles 546 and 84 access token generations per second, respectively. This
results in HERMES being 696 (with HtMAC-MiMC) and 42 (with CBC-MAC-AES) times
faster compared to in SePCAR for a single-vehicle owner access token
generation. Furthermore, we show that HERMES is practical on the vehicle side,
too, as access token operations performed on a prototype vehicle on-board unit
take only approx 62,087 ms
Instantaneous Decentralized Poker
We present efficient protocols for amortized secure multiparty computation
with penalties and secure cash distribution, of which poker is a prime example.
Our protocols have an initial phase where the parties interact with a
cryptocurrency network, that then enables them to interact only among
themselves over the course of playing many poker games in which money changes
hands.
The high efficiency of our protocols is achieved by harnessing the power of
stateful contracts. Compared to the limited expressive power of Bitcoin
scripts, stateful contracts enable richer forms of interaction between standard
secure computation and a cryptocurrency.
We formalize the stateful contract model and the security notions that our
protocols accomplish, and provide proofs using the simulation paradigm.
Moreover, we provide a reference implementation in Ethereum/Solidity for the
stateful contracts that our protocols are based on.
We also adopt our off-chain cash distribution protocols to the special case
of stateful duplex micropayment channels, which are of independent interest. In
comparison to Bitcoin based payment channels, our duplex channel implementation
is more efficient and has additional features
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