980 research outputs found
Private Multi-party Matrix Multiplication and Trust Computations
This paper deals with distributed matrix multiplication. Each player owns
only one row of both matrices and wishes to learn about one distinct row of the
product matrix, without revealing its input to the other players. We first
improve on a weighted average protocol, in order to securely compute a
dot-product with a quadratic volume of communications and linear number of
rounds. We also propose a protocol with five communication rounds, using a
Paillier-like underlying homomorphic public key cryptosystem, which is secure
in the semi-honest model or secure with high probability in the malicious
adversary model. Using ProVerif, a cryptographic protocol verification tool, we
are able to check the security of the protocol and provide a countermeasure for
each attack found by the tool. We also give a randomization method to avoid
collusion attacks. As an application, we show that this protocol enables a
distributed and secure evaluation of trust relationships in a network, for a
large class of trust evaluation schemes.Comment: Pierangela Samarati. SECRYPT 2016 : 13th International Conference on
Security and Cryptography, Lisbonne, Portugal, 26--28 Juillet 2016. 201
XONN: XNOR-based Oblivious Deep Neural Network Inference
Advancements in deep learning enable cloud servers to provide
inference-as-a-service for clients. In this scenario, clients send their raw
data to the server to run the deep learning model and send back the results.
One standing challenge in this setting is to ensure the privacy of the clients'
sensitive data. Oblivious inference is the task of running the neural network
on the client's input without disclosing the input or the result to the server.
This paper introduces XONN, a novel end-to-end framework based on Yao's Garbled
Circuits (GC) protocol, that provides a paradigm shift in the conceptual and
practical realization of oblivious inference. In XONN, the costly
matrix-multiplication operations of the deep learning model are replaced with
XNOR operations that are essentially free in GC. We further provide a novel
algorithm that customizes the neural network such that the runtime of the GC
protocol is minimized without sacrificing the inference accuracy.
We design a user-friendly high-level API for XONN, allowing expression of the
deep learning model architecture in an unprecedented level of abstraction.
Extensive proof-of-concept evaluation on various neural network architectures
demonstrates that XONN outperforms prior art such as Gazelle (USENIX
Security'18) by up to 7x, MiniONN (ACM CCS'17) by 93x, and SecureML (IEEE
S&P'17) by 37x. State-of-the-art frameworks require one round of interaction
between the client and the server for each layer of the neural network,
whereas, XONN requires a constant round of interactions for any number of
layers in the model. XONN is first to perform oblivious inference on Fitnet
architectures with up to 21 layers, suggesting a new level of scalability
compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to
perform privacy-preserving medical diagnosis.Comment: To appear in USENIX Security 201
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
SEMBA:SEcure multi-biometric authentication
Biometrics security is a dynamic research area spurred by the need to protect
personal traits from threats like theft, non-authorised distribution, reuse and
so on. A widely investigated solution to such threats consists in processing
the biometric signals under encryption, to avoid any leakage of information
towards non-authorised parties. In this paper, we propose to leverage on the
superior performance of multimodal biometric recognition to improve the
efficiency of a biometric-based authentication protocol operating on encrypted
data under the malicious security model. In the proposed protocol,
authentication relies on both facial and iris biometrics, whose representation
accuracy is specifically tailored to trade-off between recognition accuracy and
efficiency. From a cryptographic point of view, the protocol relies on SPDZ a
new multy-party computation tool designed by Damgaard et al. Experimental
results show that the multimodal protocol is faster than corresponding unimodal
protocols achieving the same accuracy
A Touch of Evil: High-Assurance Cryptographic Hardware from Untrusted Components
The semiconductor industry is fully globalized and integrated circuits (ICs)
are commonly defined, designed and fabricated in different premises across the
world. This reduces production costs, but also exposes ICs to supply chain
attacks, where insiders introduce malicious circuitry into the final products.
Additionally, despite extensive post-fabrication testing, it is not uncommon
for ICs with subtle fabrication errors to make it into production systems.
While many systems may be able to tolerate a few byzantine components, this is
not the case for cryptographic hardware, storing and computing on confidential
data. For this reason, many error and backdoor detection techniques have been
proposed over the years. So far all attempts have been either quickly
circumvented, or come with unrealistically high manufacturing costs and
complexity.
This paper proposes Myst, a practical high-assurance architecture, that uses
commercial off-the-shelf (COTS) hardware, and provides strong security
guarantees, even in the presence of multiple malicious or faulty components.
The key idea is to combine protective-redundancy with modern threshold
cryptographic techniques to build a system tolerant to hardware trojans and
errors. To evaluate our design, we build a Hardware Security Module that
provides the highest level of assurance possible with COTS components.
Specifically, we employ more than a hundred COTS secure crypto-coprocessors,
verified to FIPS140-2 Level 4 tamper-resistance standards, and use them to
realize high-confidentiality random number generation, key derivation, public
key decryption and signing. Our experiments show a reasonable computational
overhead (less than 1% for both Decryption and Signing) and an exponential
increase in backdoor-tolerance as more ICs are added
Secure and Efficient Two-party Quantum Scalar Product Protocol With Application to Privacy-preserving Matrix Multiplication
Secure two-party scalar product (S2SP) is a promising research area within
secure multiparty computation (SMC), which can solve a range of SMC problems,
such as intrusion detection, data analysis, and geometric computations.
However, existing quantum S2SP protocols are not efficient enough, and the
complexity is usually close to exponential level. In this paper, a novel secure
two-party quantum scalar product (S2QSP) protocol based on Fourier entangled
states is proposed to achieve higher efficiency. Firstly, the definition of
unconditional security under malicious models is given. And then, an honesty
verification method called Entanglement Bondage is proposed, which is used in
conjunction with the modular summation gate to resist malicious attacks. The
property of Fourier entangled states is used to calculate the scalar product
with polynomial complexity. The unconditional security of our protocol is
proved, which guarantees the privacy of all parties. In addition, we design a
privacy-preserving quantum matrix multiplication protocol based on S2QSP
protocol. By transforming matrix multiplication into a series of scalar product
processes, the product of two private matrices is calculated without revealing
any privacy. Finally, we show our protocol's feasibility in IBM Qiskit
simulator.Comment: 15 pages, 4 figure
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