6,438 research outputs found
Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs
Deep Learning as a Service (DLaaS) stands as a promising solution for
cloud-based inference applications. In this setting, the cloud has a
pre-learned model whereas the user has samples on which she wants to run the
model. The biggest concern with DLaaS is user privacy if the input samples are
sensitive data. We provide here an efficient privacy-preserving system by
employing high-end technologies such as Fully Homomorphic Encryption (FHE),
Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE,
with its widely-known feature of computing on encrypted data, empowers a wide
range of privacy-concerned applications. This comes at high cost as it requires
enormous computing power. In this paper, we show how to accelerate the
performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs
to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution
achieved a sufficient security level (> 80 bit) and reasonable classification
accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of
latency, we could classify an image in 5.16 seconds and 304.43 seconds for
MNIST and CIFAR-10, respectively. Our system can also classify a batch of
images (> 8,000) without extra overhead
k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data
Data Mining has wide applications in many areas such as banking, medicine,
scientific research and among government agencies. Classification is one of the
commonly used tasks in data mining applications. For the past decade, due to
the rise of various privacy issues, many theoretical and practical solutions to
the classification problem have been proposed under different security models.
However, with the recent popularity of cloud computing, users now have the
opportunity to outsource their data, in encrypted form, as well as the data
mining tasks to the cloud. Since the data on the cloud is in encrypted form,
existing privacy preserving classification techniques are not applicable. In
this paper, we focus on solving the classification problem over encrypted data.
In particular, we propose a secure k-NN classifier over encrypted data in the
cloud. The proposed k-NN protocol protects the confidentiality of the data,
user's input query, and data access patterns. To the best of our knowledge, our
work is the first to develop a secure k-NN classifier over encrypted data under
the semi-honest model. Also, we empirically analyze the efficiency of our
solution through various experiments.Comment: 29 pages, 2 figures, 3 tables arXiv admin note: substantial text
overlap with arXiv:1307.482
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
Block encryption of quantum messages
In modern cryptography, block encryption is a fundamental cryptographic
primitive. However, it is impossible for block encryption to achieve the same
security as one-time pad. Quantum mechanics has changed the modern
cryptography, and lots of researches have shown that quantum cryptography can
outperform the limitation of traditional cryptography.
This article proposes a new constructive mode for private quantum encryption,
named , which is a very simple method to construct quantum
encryption from classical primitive. Based on mode, we
construct a quantum block encryption (QBE) scheme from pseudorandom functions.
If the pseudorandom functions are standard secure, our scheme is
indistinguishable encryption under chosen plaintext attack. If the pseudorandom
functions are permutation on the key space, our scheme can achieve perfect
security. In our scheme, the key can be reused and the randomness cannot, so a
-bit key can be used in an exponential number of encryptions, where the
randomness will be refreshed in each time of encryption. Thus -bit key can
perfectly encrypt qubits, and the perfect secrecy would not be broken
if the -bit key is reused for only exponential times.
Comparing with quantum one-time pad (QOTP), our scheme can be the same secure
as QOTP, and the secret key can be reused (no matter whether the eavesdropping
exists or not). Thus, the limitation of perfectly secure encryption (Shannon's
theory) is broken in the quantum setting. Moreover, our scheme can be viewed as
a positive answer to the open problem in quantum cryptography "how to
unconditionally reuse or recycle the whole key of private-key quantum
encryption". In order to physically implement the QBE scheme, we only need to
implement two kinds of single-qubit gates (Pauli gate and Hadamard gate),
so it is within reach of current quantum technology.Comment: 13 pages, 1 figure. Prior version appears in
eprint.iacr.org(iacr/2017/1247). This version adds some analysis about
multiple-message encryption, and modifies lots of contents. There are no
changes about the fundamental result
Equivalence-based Security for Querying Encrypted Databases: Theory and Application to Privacy Policy Audits
Motivated by the problem of simultaneously preserving confidentiality and
usability of data outsourced to third-party clouds, we present two different
database encryption schemes that largely hide data but reveal enough
information to support a wide-range of relational queries. We provide a
security definition for database encryption that captures confidentiality based
on a notion of equivalence of databases from the adversary's perspective. As a
specific application, we adapt an existing algorithm for finding violations of
privacy policies to run on logs encrypted under our schemes and observe low to
moderate overheads.Comment: CCS 2015 paper technical report, in progres
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
- …