1,596 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
Combining behavioural types with security analysis
Today's software systems are highly distributed and interconnected, and they
increasingly rely on communication to achieve their goals; due to their
societal importance, security and trustworthiness are crucial aspects for the
correctness of these systems. Behavioural types, which extend data types by
describing also the structured behaviour of programs, are a widely studied
approach to the enforcement of correctness properties in communicating systems.
This paper offers a unified overview of proposals based on behavioural types
which are aimed at the analysis of security properties
Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare
Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare
A Hybrid Approach to Privacy-Preserving Federated Learning
Federated learning facilitates the collaborative training of models without
the sharing of raw data. However, recent attacks demonstrate that simply
maintaining data locality during training processes does not provide sufficient
privacy guarantees. Rather, we need a federated learning system capable of
preventing inference over both the messages exchanged during training and the
final trained model while ensuring the resulting model also has acceptable
predictive accuracy. Existing federated learning approaches either use secure
multiparty computation (SMC) which is vulnerable to inference or differential
privacy which can lead to low accuracy given a large number of parties with
relatively small amounts of data each. In this paper, we present an alternative
approach that utilizes both differential privacy and SMC to balance these
trade-offs. Combining differential privacy with secure multiparty computation
enables us to reduce the growth of noise injection as the number of parties
increases without sacrificing privacy while maintaining a pre-defined rate of
trust. Our system is therefore a scalable approach that protects against
inference threats and produces models with high accuracy. Additionally, our
system can be used to train a variety of machine learning models, which we
validate with experimental results on 3 different machine learning algorithms.
Our experiments demonstrate that our approach out-performs state of the art
solutions
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
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