5,544 research outputs found
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Performing machine learning (ML) computation on private data while
maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an
emergent field of research. Recently, PPML has seen a visible shift towards the
adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy
computation that it entails. In the SOC paradigm, computation is outsourced to
a set of powerful and specially equipped servers that provide service on a
pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for
a range of ML algorithms in SOC setting, that guarantees output delivery to the
users irrespective of any adversarial behaviour. Robustness, a highly desirable
feature, evokes user participation without the fear of denial of service.
At the heart of our framework lies a highly-efficient, maliciously-secure,
three-party computation (3PC) over rings that provides guaranteed output
delivery (GOD) in the honest-majority setting. To the best of our knowledge,
SWIFT is the first robust and efficient PPML framework in the 3PC setting.
SWIFT is as fast as (and is strictly better in some cases than) the best-known
3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We
extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as
fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20)
and twice faster than the best-known robust 4PC framework FLASH (Byali et al.
PETS'20).
We demonstrate our framework's practical relevance by benchmarking popular ML
algorithms such as Logistic Regression and deep Neural Networks such as VGG16
and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results
testify to our claims that we provide improved security guarantee while
incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear
in USENIX Security 202
High-precision Secure Computation of Satellite Collision Probabilities
The costs of designing, building, launching and maintaining satellites make satellite operators extremely motivated to protect their on-orbit assets. Unfortunately, privacy concerns present a serious barrier to coordination between different operators. One obstacle to improving safety arises because operators view the trajectories of their satellites as private, and refuse to share this private information with other operators. Without data-sharing, preventing collisions between satellites becomes a challenging task.
A 2014 report from the RAND Corporation proposed using cryptographic tools from the domain of secure Multiparty Computation (MPC) to allow satellite operators to calculate collision probabilities (conjunction analyses) without sharing private information about the trajectories of their satellites.
In this work, we report on the design and implementation of a powerful new MPC framework for high-precision arithmetic on real-valued variables in a two-party setting where, unlike previous works, there is no honest majority, and where the players are not assumed to be semi-honest. We show how to apply this new solution in the domain of securely computing conjunction analyses. Our solution extends existing protocols, in particular the integer-based Goldreich-Micali-Wigderson (GMW) protocol, whereby we use combine and optimize GMW with Garbled Circuits (GC). We prove security of our protocol in the two party, semi-honest setting, assuming only the existence of one-way functions and Oblivious Transfer (the OT-hybrid model). The protocol allows a pair of satellite operators to compute the probability that their satellites will collide without sharing their underlying private orbital information. Techniques developed in this paper would potentially have a wide impact on general secure numerical analysis computations. We also show how to strengthen our construction with standard arithmetic message-authentication-codes (MACs) to enforce honest behavior beyond the semi-honest setting.
Computing a conjunction analysis requires numerically estimating a complex double integral to a high degree of precision. The complexity of the calculation, and the possibility of numeric instability presents many challenges for MPC protocols which typically model calculations as simple (integer) arithmetic or binary circuits.
Our secure numerical integration routines are extremely stable and efficient, and our secure conjunction analysis protocol takes only a few minutes to run on a commodity laptop
SPDZ2k: Efficient MPC mod 2^k for Dishonest Majority
Most multi-party computation protocols allow secure computation of arithmetic circuits over a finite field, such as the integers modulo a prime.
In the more natural setting of integer computations modulo , which are useful for simplifying implementations and applications, no solutions with active security are known unless the majority of the participants are honest.
We present a new scheme for information-theoretic MACs that are homomorphic modulo , and are as efficient as the well-known standard solutions that are homomorphic over fields.
We apply this to construct an MPC protocol for dishonest majority in the preprocessing model that has efficiency comparable to the well-known SPDZ protocol (Damgård et al., CRYPTO 2012), with operations modulo instead of over a field.
We also construct a matching preprocessing protocol based on oblivious transfer, which is in the style of the MASCOT protocol (Keller et al., CCS 2016) and almost as efficient
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
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
Computer-aided proofs for multiparty computation with active security
Secure multi-party computation (MPC) is a general cryptographic technique
that allows distrusting parties to compute a function of their individual
inputs, while only revealing the output of the function. It has found
applications in areas such as auctioning, email filtering, and secure
teleconference. Given its importance, it is crucial that the protocols are
specified and implemented correctly. In the programming language community it
has become good practice to use computer proof assistants to verify correctness
proofs. In the field of cryptography, EasyCrypt is the state of the art proof
assistant. It provides an embedded language for probabilistic programming,
together with a specialized logic, embedded into an ambient general purpose
higher-order logic. It allows us to conveniently express cryptographic
properties. EasyCrypt has been used successfully on many applications,
including public-key encryption, signatures, garbled circuits and differential
privacy. Here we show for the first time that it can also be used to prove
security of MPC against a malicious adversary. We formalize additive and
replicated secret sharing schemes and apply them to Maurer's MPC protocol for
secure addition and multiplication. Our method extends to general polynomial
functions. We follow the insights from EasyCrypt that security proofs can be
often be reduced to proofs about program equivalence, a topic that is well
understood in the verification of programming languages. In particular, we show
that in the passive case the non-interference-based definition is equivalent to
a standard game-based security definition. For the active case we provide a new
NI definition, which we call input independence
Secure -ish Nearest Neighbors Classifier
In machine learning, classifiers are used to predict a class of a given query
based on an existing (classified) database. Given a database S of n
d-dimensional points and a d-dimensional query q, the k-nearest neighbors (kNN)
classifier assigns q with the majority class of its k nearest neighbors in S.
In the secure version of kNN, S and q are owned by two different parties that
do not want to share their data. Unfortunately, all known solutions for secure
kNN either require a large communication complexity between the parties, or are
very inefficient to run.
In this work we present a classifier based on kNN, that can be implemented
efficiently with homomorphic encryption (HE). The efficiency of our classifier
comes from a relaxation we make on kNN, where we allow it to consider kappa
nearest neighbors for kappa ~ k with some probability. We therefore call our
classifier k-ish Nearest Neighbors (k-ish NN).
The success probability of our solution depends on the distribution of the
distances from q to S and increase as its statistical distance to Gaussian
decrease.
To implement our classifier we introduce the concept of double-blinded
coin-toss. In a doubly-blinded coin-toss the success probability as well as the
output of the toss are encrypted. We use this coin-toss to efficiently
approximate the average and variance of the distances from q to S. We believe
these two techniques may be of independent interest.
When implemented with HE, the k-ish NN has a circuit depth that is
independent of n, therefore making it scalable. We also implemented our
classifier in an open source library based on HELib and tested it on a breast
tumor database. The accuracy of our classifier (F_1 score) were 98\% and
classification took less than 3 hours compared to (estimated) weeks in current
HE implementations
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