9,578 research outputs found
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
Secure secret sharing in the cloud
In this paper, we show how a dealer with limited resources is possible to share the secrets to players via an untrusted cloud server without compromising the privacy of the secrets. This scheme permits a batch of two secret messages to be shared to two players in such a way that the secrets are reconstructable if and only if two of them collaborate. An individual share reveals absolutely no information about the secrets to the player. The secret messages are obfuscated by encryption and thus give no information to the cloud server. Furthermore, the scheme is compatible with the Paillier cryptosystem and other cryptosystems of the same type. In light of the recent developments in privacy-preserving watermarking technology, we further model the proposed scheme as a variant of reversible watermarking in the encrypted domain
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
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 participant’s MapReduce cluster as well as across all the participants’ 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
Keeping Authorities "Honest or Bust" with Decentralized Witness Cosigning
The secret keys of critical network authorities - such as time, name,
certificate, and software update services - represent high-value targets for
hackers, criminals, and spy agencies wishing to use these keys secretly to
compromise other hosts. To protect authorities and their clients proactively
from undetected exploits and misuse, we introduce CoSi, a scalable witness
cosigning protocol ensuring that every authoritative statement is validated and
publicly logged by a diverse group of witnesses before any client will accept
it. A statement S collectively signed by W witnesses assures clients that S has
been seen, and not immediately found erroneous, by those W observers. Even if S
is compromised in a fashion not readily detectable by the witnesses, CoSi still
guarantees S's exposure to public scrutiny, forcing secrecy-minded attackers to
risk that the compromise will soon be detected by one of the W witnesses.
Because clients can verify collective signatures efficiently without
communication, CoSi protects clients' privacy, and offers the first
transparency mechanism effective against persistent man-in-the-middle attackers
who control a victim's Internet access, the authority's secret key, and several
witnesses' secret keys. CoSi builds on existing cryptographic multisignature
methods, scaling them to support thousands of witnesses via signature
aggregation over efficient communication trees. A working prototype
demonstrates CoSi in the context of timestamping and logging authorities,
enabling groups of over 8,000 distributed witnesses to cosign authoritative
statements in under two seconds.Comment: 20 pages, 7 figure
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