3,285 research outputs found
On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects
The Internet of Things (IoT) will be a main data generation infrastructure
for achieving better system intelligence. This paper considers the design and
implementation of a practical privacy-preserving collaborative learning scheme,
in which a curious learning coordinator trains a better machine learning model
based on the data samples contributed by a number of IoT objects, while the
confidentiality of the raw forms of the training data is protected against the
coordinator. Existing distributed machine learning and data encryption
approaches incur significant computation and communication overhead, rendering
them ill-suited for resource-constrained IoT objects. We study an approach that
applies independent Gaussian random projection at each IoT object to obfuscate
data and trains a deep neural network at the coordinator based on the projected
data from the IoT objects. This approach introduces light computation overhead
to the IoT objects and moves most workload to the coordinator that can have
sufficient computing resources. Although the independent projections performed
by the IoT objects address the potential collusion between the curious
coordinator and some compromised IoT objects, they significantly increase the
complexity of the projected data. In this paper, we leverage the superior
learning capability of deep learning in capturing sophisticated patterns to
maintain good learning performance. Extensive comparative evaluation shows that
this approach outperforms other lightweight approaches that apply additive
noisification for differential privacy and/or support vector machines for
learning in the applications with light data pattern complexities.Comment: 12 pages,IOTDI 201
Paralinguistic Privacy Protection at the Edge
Voice user interfaces and digital assistants are rapidly entering our lives
and becoming singular touch points spanning our devices. These always-on
services capture and transmit our audio data to powerful cloud services for
further processing and subsequent actions. Our voices and raw audio signals
collected through these devices contain a host of sensitive paralinguistic
information that is transmitted to service providers regardless of deliberate
or false triggers. As our emotional patterns and sensitive attributes like our
identity, gender, mental well-being, are easily inferred using deep acoustic
models, we encounter a new generation of privacy risks by using these services.
One approach to mitigate the risk of paralinguistic-based privacy breaches is
to exploit a combination of cloud-based processing with privacy-preserving,
on-device paralinguistic information learning and filtering before transmitting
voice data. In this paper we introduce EDGY, a configurable, lightweight,
disentangled representation learning framework that transforms and filters
high-dimensional voice data to identify and contain sensitive attributes at the
edge prior to offloading to the cloud. We evaluate EDGY's on-device performance
and explore optimization techniques, including model quantization and knowledge
distillation, to enable private, accurate and efficient representation learning
on resource-constrained devices. Our results show that EDGY runs in tens of
milliseconds with 0.2% relative improvement in ABX score or minimal performance
penalties in learning linguistic representations from raw voice signals, using
a CPU and a single-core ARM processor without specialized hardware.Comment: 14 pages, 7 figures. arXiv admin note: text overlap with
arXiv:2007.1506
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