1,468 research outputs found
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
Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm
Reverberation, which is generally caused by sound reflections from walls,
ceilings, and floors, can result in severe performance degradation of acoustic
applications. Due to a complicated combination of attenuation and time-delay
effects, the reverberation property is difficult to characterize, and it
remains a challenging task to effectively retrieve the anechoic speech signals
from reverberation ones. In the present study, we proposed a novel integrated
deep and ensemble learning algorithm (IDEA) for speech dereverberation. The
IDEA consists of offline and online phases. In the offline phase, we train
multiple dereverberation models, each aiming to precisely dereverb speech
signals in a particular acoustic environment; then a unified fusion function is
estimated that aims to integrate the information of multiple dereverberation
models. In the online phase, an input utterance is first processed by each of
the dereverberation models. The outputs of all models are integrated
accordingly to generate the final anechoic signal. We evaluated the IDEA on
designed acoustic environments, including both matched and mismatched
conditions of the training and testing data. Experimental results confirm that
the proposed IDEA outperforms single deep-neural-network-based dereverberation
model with the same model architecture and training data
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