8,048 research outputs found
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Deep Learning has recently become hugely popular in machine learning,
providing significant improvements in classification accuracy in the presence
of highly-structured and large databases.
Researchers have also considered privacy implications of deep learning.
Models are typically trained in a centralized manner with all the data being
processed by the same training algorithm. If the data is a collection of users'
private data, including habits, personal pictures, geographical positions,
interests, and more, the centralized server will have access to sensitive
information that could potentially be mishandled. To tackle this problem,
collaborative deep learning models have recently been proposed where parties
locally train their deep learning structures and only share a subset of the
parameters in the attempt to keep their respective training sets private.
Parameters can also be obfuscated via differential privacy (DP) to make
information extraction even more challenging, as proposed by Shokri and
Shmatikov at CCS'15.
Unfortunately, we show that any privacy-preserving collaborative deep
learning is susceptible to a powerful attack that we devise in this paper. In
particular, we show that a distributed, federated, or decentralized deep
learning approach is fundamentally broken and does not protect the training
sets of honest participants. The attack we developed exploits the real-time
nature of the learning process that allows the adversary to train a Generative
Adversarial Network (GAN) that generates prototypical samples of the targeted
training set that was meant to be private (the samples generated by the GAN are
intended to come from the same distribution as the training data).
Interestingly, we show that record-level DP applied to the shared parameters of
the model, as suggested in previous work, is ineffective (i.e., record-level DP
is not designed to address our attack).Comment: ACM CCS'17, 16 pages, 18 figure
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
User-generated data is crucial to predictive modeling in many applications.
With a web/mobile/wearable interface, a data owner can continuously record data
generated by distributed users and build various predictive models from the
data to improve their operations, services, and revenue. Due to the large size
and evolving nature of users data, data owners may rely on public cloud service
providers (Cloud) for storage and computation scalability. Exposing sensitive
user-generated data and advanced analytic models to Cloud raises privacy
concerns. We present a confidential learning framework, SecureBoost, for data
owners that want to learn predictive models from aggregated user-generated data
but offload the storage and computational burden to Cloud without having to
worry about protecting the sensitive data. SecureBoost allows users to submit
encrypted or randomly masked data to designated Cloud directly. Our framework
utilizes random linear classifiers (RLCs) as the base classifiers in the
boosting framework to dramatically simplify the design of the proposed
confidential boosting protocols, yet still preserve the model quality. A
Cryptographic Service Provider (CSP) is used to assist the Cloud's processing,
reducing the complexity of the protocol constructions. We present two
constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of
homomorphic encryption, garbled circuits, and random masking to achieve both
security and efficiency. For a boosted model, Cloud learns only the RLCs and
the CSP learns only the weights of the RLCs. Finally, the data owner collects
the two parts to get the complete model. We conduct extensive experiments to
understand the quality of the RLC-based boosting and the cost distribution of
the constructions. Our results show that SecureBoost can efficiently learn
high-quality boosting models from protected user-generated data
Quantum-inspired Machine Learning on high-energy physics data
Tensor Networks, a numerical tool originally designed for simulating quantum
many-body systems, have recently been applied to solve Machine Learning
problems. Exploiting a tree tensor network, we apply a quantum-inspired machine
learning technique to a very important and challenging big data problem in high
energy physics: the analysis and classification of data produced by the Large
Hadron Collider at CERN. In particular, we present how to effectively classify
so-called b-jets, jets originating from b-quarks from proton-proton collisions
in the LHCb experiment, and how to interpret the classification results. We
exploit the Tensor Network approach to select important features and adapt the
network geometry based on information acquired in the learning process.
Finally, we show how to adapt the tree tensor network to achieve optimal
precision or fast response in time without the need of repeating the learning
process. These results pave the way to the implementation of high-frequency
real-time applications, a key ingredient needed among others for current and
future LHCb event classification able to trigger events at the tens of MHz
scale.Comment: 13 pages, 4 figure
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