1,920 research outputs found
Quantum Fully Homomorphic Encryption With Verification
Fully-homomorphic encryption (FHE) enables computation on encrypted data
while maintaining secrecy. Recent research has shown that such schemes exist
even for quantum computation. Given the numerous applications of classical FHE
(zero-knowledge proofs, secure two-party computation, obfuscation, etc.) it is
reasonable to hope that quantum FHE (or QFHE) will lead to many new results in
the quantum setting. However, a crucial ingredient in almost all applications
of FHE is circuit verification. Classically, verification is performed by
checking a transcript of the homomorphic computation. Quantumly, this strategy
is impossible due to no-cloning. This leads to an important open question: can
quantum computations be delegated and verified in a non-interactive manner? In
this work, we answer this question in the affirmative, by constructing a scheme
for QFHE with verification (vQFHE). Our scheme provides authenticated
encryption, and enables arbitrary polynomial-time quantum computations without
the need of interaction between client and server. Verification is almost
entirely classical; for computations that start and end with classical states,
it is completely classical. As a first application, we show how to construct
quantum one-time programs from classical one-time programs and vQFHE.Comment: 30 page
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
Verifiable Encodings for Secure Homomorphic Analytics
Homomorphic encryption, which enables the execution of arithmetic operations
directly on ciphertexts, is a promising solution for protecting privacy of
cloud-delegated computations on sensitive data. However, the correctness of the
computation result is not ensured. We propose two error detection encodings and
build authenticators that enable practical client-verification of cloud-based
homomorphic computations under different trade-offs and without compromising on
the features of the encryption algorithm. Our authenticators operate on top of
trending ring learning with errors based fully homomorphic encryption schemes
over the integers. We implement our solution in VERITAS, a ready-to-use system
for verification of outsourced computations executed over encrypted data. We
show that contrary to prior work VERITAS supports verification of any
homomorphic operation and we demonstrate its practicality for various
applications, such as ride-hailing, genomic-data analysis, encrypted search,
and machine-learning training and inference.Comment: update authors, typos corrected, scheme update
Privacy-preserving biometric matching using homomorphic encryption
Biometric matching involves storing and processing sensitive user
information. Maintaining the privacy of this data is thus a major challenge,
and homomorphic encryption offers a possible solution. We propose a
privacy-preserving biometrics-based authentication protocol based on fully
homomorphic encryption, where the biometric sample for a user is gathered by a
local device but matched against a biometric template by a remote server
operating solely on encrypted data. The design ensures that 1) the user's
sensitive biometric data remains private, and 2) the user and client device are
securely authenticated to the server. A proof-of-concept implementation
building on the TFHE library is also presented, which includes the underlying
basic operations needed to execute the biometric matching. Performance results
from the implementation show how complex it is to make FHE practical in this
context, but it appears that, with implementation optimisations and
improvements, the protocol could be used for real-world applications
Security and Privacy Issues of Big Data
This chapter revises the most important aspects in how computing
infrastructures should be configured and intelligently managed to fulfill the
most notably security aspects required by Big Data applications. One of them is
privacy. It is a pertinent aspect to be addressed because users share more and
more personal data and content through their devices and computers to social
networks and public clouds. So, a secure framework to social networks is a very
hot topic research. This last topic is addressed in one of the two sections of
the current chapter with case studies. In addition, the traditional mechanisms
to support security such as firewalls and demilitarized zones are not suitable
to be applied in computing systems to support Big Data. SDN is an emergent
management solution that could become a convenient mechanism to implement
security in Big Data systems, as we show through a second case study at the end
of the chapter. This also discusses current relevant work and identifies open
issues.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
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