920 research outputs found
Better Preprocessing for Secure Multiparty Computation
We present techniques and protocols for the preprocessing of secure multiparty computation (MPC), focusing on the so-called SPDZ MPC scheme SPDZ and its derivatives. These MPC schemes consist of a so-called preprocessing or offline phase where correlated randomness is generated that is independent of the inputs and the evaluated function, and an online phase where such correlated randomness is consumed to securely and efficiently evaluate circuits. In the recent years, it has been shown that such protocols turn out to be very efficient in practice.
While much research has been conducted towards optimizing the online phase of the MPC protocols, there seems to have been less focus on the offline phase of such protocols. With this work, we want to close this gap and give a toolbox of techniques that aim at optimizing the preprocessing.
We support both instantiations over small fields and large rings using somewhat homomorphic encryption and the Paillier cryptosystem, respectively. In the case of small fields, we show how the preprocessing overhead can basically be made independent of the field characteristic and present a more efficient (amortized) zero-knowledge proof of plaintext knowledge. In the case of large rings, we present a protocol based on the Paillier cryptosystem which has a lower message complexity than previous protocols and employs more efficient zero-knowledge proofs that, to the best of our knowledge, were not presented in previous work
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
Conclave: secure multi-party computation on big data (extended TR)
Secure Multi-Party Computation (MPC) allows mutually distrusting parties to
run joint computations without revealing private data. Current MPC algorithms
scale poorly with data size, which makes MPC on "big data" prohibitively slow
and inhibits its practical use.
Many relational analytics queries can maintain MPC's end-to-end security
guarantee without using cryptographic MPC techniques for all operations.
Conclave is a query compiler that accelerates such queries by transforming them
into a combination of data-parallel, local cleartext processing and small MPC
steps. When parties trust others with specific subsets of the data, Conclave
applies new hybrid MPC-cleartext protocols to run additional steps outside of
MPC and improve scalability further.
Our Conclave prototype generates code for cleartext processing in Python and
Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave
scales to data sets between three and six orders of magnitude larger than
state-of-the-art MPC frameworks support on their own. Thanks to its hybrid
protocols, Conclave also substantially outperforms SMCQL, the most similar
existing system.Comment: Extended technical report for EuroSys 2019 pape
Privacy Preserving Computation in Home Loans using the FRESCO Framework
Secure Multiparty Computation (SMC) is a subfield of cryptography that allows multiple parties to compute jointly on a function without revealing their inputs to others. The technology is able to solve potential privacy issues that arises when a trusted third party is involved, like a server. This paper aims to evaluate implementations of Secure Multiparty Computation and its viability for practical use. The paper also seeks to understand and state the challenges and concepts of Secure Multiparty Computation through the construction of a home loan calculation application. Encryption over MPC is done within 2 to 2.5 Seconds. Up to 10K addition operations, MPC system performs very well and most applications will be sufficient within 10K additions
Implementation of a Secure Multiparty Computation Protocol
Secure multiparty computation (SMC) allows a set of parties to jointly compute a function on private inputs such that, they learn only the output of the function, and the correctness of the output is guaranteed even when a subset of the parties is controlled by an adversary. SMC allows data to be kept in an uncompromisable form and still be useful, and it also gives new meaning to data ownership, allowing data to be shared in a useful way while retaining its privacy. Thus, applications of SMC hold promise for addressing some of the security issues information-driven societies struggle with.
In this thesis, we implement two SMC protocols. Our primary objective is to gain a solid understanding of the basic concepts related to SMC. We present a brief survey of the field, with focus on SMC based on secret sharing. In addition to the protocol im- plementations, we implement circuit randomization, a common technique for efficiency improvement. The implemented protocols are run on a simulator to securely evaluate some simple arithmetic functions, and the round complexities of the implemented protocols are compared. Finally, we attempt to extend the implementation to support more general computations
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