40,947 research outputs found
LEGO for Two Party Secure Computation
The first and still most popular solution for secure two-party
computation relies on Yao\u27s garbled circuits. Unfortunately, Yao\u27s
construction provide security only against passive adversaries.
Several constructions (zero-knowledge compiler, cut-and-choose) are
known in order to provide security against active adversaries, but
most of them are not efficient enough to be considered practical. In
this paper we propose a new approach called LEGO (Large Efficient
Garbled-circuit Optimization) for two-party computation, which allows
to construct more efficient protocols secure against active
adversaries. The basic idea is the following: Alice constructs and
provides Bob a set of garbled NAND gates. A fraction of them is
checked by Alice giving Bob the randomness used to construct
them. When the check goes through, with overwhelming probability there
are very few bad gates among the non-checked gates. These gates Bob
permutes and connects to a Yao circuit, according to a fault-tolerant
circuit design which computes the desired function even in the
presence of a few random faulty gates. Finally he evaluates this Yao
circuit in the usual way.
For large circuits, our protocol offers better performance than any
other existing protocol.
The protocol is universally composable (UC) in the OT-hybrid model
Programming support for an integrated multi-party computation and MapReduce infrastructure
We describe and present a prototype of a distributed computational infrastructure and associated high-level programming language that allow multiple parties to leverage their own computational resources capable of supporting MapReduce [1] operations in combination with multi-party computation (MPC). Our architecture allows a programmer to author and compile a protocol using a uniform collection of standard constructs, even when that protocol involves computations that take place locally within each participantâs MapReduce cluster as well as across all the participants using an MPC protocol. The highlevel programming language provided to the user is accompanied by static analysis algorithms that allow the programmer to reason about the efficiency of the protocol before compiling and running it. We present two example applications demonstrating how such an infrastructure can be employed.This work was supported in part
by NSF Grants: #1430145, #1414119, #1347522, and #1012798
Scather: programming with multi-party computation and MapReduce
We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of supporting MapReduce [27] operations) in concert with multi-party computation (MPC) to execute statistical analysis algorithms that have privacy-preserving properties. Our architecture allows a data analyst unfamiliar with MPC to: (1) author an analysis algorithm that is agnostic with regard to data privacy policies, (2) to use an automated process to derive algorithm implementation variants that have different privacy and performance properties, and (3) to compile those implementation variants so that they can be deployed on an infrastructures that allows computations to take place locally within each participantâs MapReduce cluster as well as across all the participantsâ clusters using an MPC protocol. We describe implementation details of the architecture, discuss and demonstrate how the formal framework enables the exploration of tradeoffs between the efficiency and privacy properties of an analysis algorithm, and present two example applications that illustrate how such an infrastructure can be utilized in practice.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798
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
Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation
The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand
The Crypto-democracy and the Trustworthy
In the current architecture of the Internet, there is a strong asymmetry in
terms of power between the entities that gather and process personal data
(e.g., major Internet companies, telecom operators, cloud providers, ...) and
the individuals from which this personal data is issued. In particular,
individuals have no choice but to blindly trust that these entities will
respect their privacy and protect their personal data. In this position paper,
we address this issue by proposing an utopian crypto-democracy model based on
existing scientific achievements from the field of cryptography. More
precisely, our main objective is to show that cryptographic primitives,
including in particular secure multiparty computation, offer a practical
solution to protect privacy while minimizing the trust assumptions. In the
crypto-democracy envisioned, individuals do not have to trust a single physical
entity with their personal data but rather their data is distributed among
several institutions. Together these institutions form a virtual entity called
the Trustworthy that is responsible for the storage of this data but which can
also compute on it (provided first that all the institutions agree on this).
Finally, we also propose a realistic proof-of-concept of the Trustworthy, in
which the roles of institutions are played by universities. This
proof-of-concept would have an important impact in demonstrating the
possibilities offered by the crypto-democracy paradigm.Comment: DPM 201
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