78,895 research outputs found
Enable Portrait Privacy Protection in Photo Capturing and Sharing
The wide adoption of wearable smart devices with onboard cameras greatly
increases people's concern on privacy infringement. Here we explore the
possibility of easing persons from photos captured by smart devices according
to their privacy protection requirements. To make this work, we need to address
two challenges: 1) how to let users explicitly express their privacy protection
intention, and 2) how to associate the privacy requirements with persons in
captured photos accurately and efficiently. Furthermore, the association
process itself should not cause portrait information leakage and should be
accomplished in a privacy-preserving way. In this work, we design, develop, and
evaluate a protocol, that enables a user to flexibly express her privacy
requirement and empowers the photo service provider (or image taker) to exert
the privacy protection policy.Leveraging the visual distinguishability of
people in the field-of-view and the dimension-order-independent property of
vector similarity measurement, we achieves high accuracy and low overhead.
We implement a prototype system, and our evaluation results on both the
trace-driven and real-life experiments confirm the feasibility and efficiency
of our system.Comment: 9 pages, 8 figure
Parity Models: A General Framework for Coding-Based Resilience in ML Inference
Machine learning models are becoming the primary workhorses for many
applications. Production services deploy models through prediction serving
systems that take in queries and return predictions by performing inference on
machine learning models. In order to scale to high query rates, prediction
serving systems are run on many machines in cluster settings, and thus are
prone to slowdowns and failures that inflate tail latency and cause violations
of strict latency targets. Current approaches to reducing tail latency are
inadequate for the latency targets of prediction serving, incur high resource
overhead, or are inapplicable to the computations performed during inference.
We present ParM, a novel, general framework for making use of ideas from
erasure coding and machine learning to achieve low-latency, resource-efficient
resilience to slowdowns and failures in prediction serving systems. ParM
encodes multiple queries together into a single parity query and performs
inference on the parity query using a parity model. A decoder uses the output
of a parity model to reconstruct approximations of unavailable predictions.
ParM uses neural networks to learn parity models that enable simple, fast
encoders and decoders to reconstruct unavailable predictions for a variety of
inference tasks such as image classification, speech recognition, and object
localization. We build ParM atop an open-source prediction serving system and
through extensive evaluation show that ParM improves overall accuracy in the
face of unavailability with low latency while using 2-4 less additional
resources than replication-based approaches. ParM reduces the gap between
99.9th percentile and median latency by up to compared to
approaches that use an equal amount of resources, while maintaining the same
median.Comment: This paper is superseded by the ACM SOSP 2019 paper "Parity Models:
Erasure-Coded Resilience for Prediction Serving Systems
Privacy-preserving Machine Learning through Data Obfuscation
As machine learning becomes a practice and commodity, numerous cloud-based
services and frameworks are provided to help customers develop and deploy
machine learning applications. While it is prevalent to outsource model
training and serving tasks in the cloud, it is important to protect the privacy
of sensitive samples in the training dataset and prevent information leakage to
untrusted third parties. Past work have shown that a malicious machine learning
service provider or end user can easily extract critical information about the
training samples, from the model parameters or even just model outputs.
In this paper, we propose a novel and generic methodology to preserve the
privacy of training data in machine learning applications. Specifically we
introduce an obfuscate function and apply it to the training data before
feeding them to the model training task. This function adds random noise to
existing samples, or augments the dataset with new samples. By doing so
sensitive information about the properties of individual samples, or
statistical properties of a group of samples, is hidden. Meanwhile the model
trained from the obfuscated dataset can still achieve high accuracy. With this
approach, the customers can safely disclose the data or models to third-party
providers or end users without the need to worry about data privacy. Our
experiments show that this approach can effective defeat four existing types of
machine learning privacy attacks at negligible accuracy cost
BAMCloud: A Cloud Based Mobile Biometric Authentication Framework
With an exponential increase in number of users switching to mobile banking,
various countries are adopting biometric solutions as security measures. The
main reason for biometric technologies becoming more common in the everyday
lives of consumers is because of the facility to easily capture biometric data
in real time, using their mobile phones. Biometric technologies are providing
the potential security framework to make banking more convenient and secure
than it has ever been. At the same time, the exponential growth of enrollment
in the biometric system produces massive amount of high dimensionality data
that leads to degradation in the performance of the mobile banking systems.
Therefore, in order to overcome the performance issues arising due to this data
deluge, this paper aims to propose a distributed mobile biometric system based
on a high performance cluster Cloud. High availability, better time efficiency
and scalability are some of the added advantages of using the proposed system.
In this paper a Cloud based mobile biometric authentication framework
(BAMCloud) is proposed that uses dynamic signatures and performs
authentication. It includes the steps involving data capture using any handheld
mobile device, then storage, preprocessing and training the system in a
distributed manner over Cloud. For this purpose we have implemented it using
MapReduce on Hadoop platform and for training Levenberg-Marquardt
backpropagation neural network has been used. Moreover, the methodology adopted
is very novel as it achieves a speedup of 8.5x and a performance of 96.23%.
Furthermore, the cost benefit analysis of the implemented system shows that the
cost of implementation and execution of the system is lesser than the existing
ones. The experiments demonstrate that the better performance is achieved by
proposed framework as compared to the other methods used in the recent
literature
Exploring Computation-Communication Tradeoffs in Camera Systems
Cameras are the defacto sensor. The growing demand for real-time and
low-power computer vision, coupled with trends towards high-efficiency
heterogeneous systems, has given rise to a wide range of image processing
acceleration techniques at the camera node and in the cloud. In this paper, we
characterize two novel camera systems that use acceleration techniques to push
the extremes of energy and performance scaling, and explore the
computation-communication tradeoffs in their design. The first case study
targets a camera system designed to detect and authenticate individual faces,
running solely on energy harvested from RFID readers. We design a
multi-accelerator SoC design operating in the sub-mW range, and evaluate it
with real-world workloads to show performance and energy efficiency
improvements over a general purpose microprocessor. The second camera system
supports a 16-camera rig processing over 32 Gb/s of data to produce real-time
3D-360 degree virtual reality video. We design a multi-FPGA processing pipeline
that outperforms CPU and GPU configurations by up to 10x in computation time,
producing panoramic stereo video directly from the camera rig at 30 frames per
second. We find that an early data reduction step, either before complex
processing or offloading, is the most critical optimization for in-camera
systems
Clome: The Practical Implications of a Cloud-based Smart Home
A rich body of work in recent years has advocated the use of cloud
technologies within a home environment, but nothing has materialized so far in
terms of real-world implementations. In this paper, we argue that this is due
to the fact that none of these proposals have addressed some of the practical
challenges of moving home applications to the cloud. Specifically, we discuss
the pragmatic implications of moving to the cloud including, data and
information security, increase in network traffic, and fault tolerance. To
elicit discussion, we take a clean-slate approach and introduce a
proof-of-concept smart home, dubbed Clome, that decouples non-trivial
computation from home applications and transfers it to the cloud. We also
discuss how a Clome-like smart home with decentralized processing and storage
can be enabled through OpenFlow programmable switches, home-centric programming
platforms, and thin-client computing
Privacy-Preserving Deep Inference for Rich User Data on The Cloud
Deep neural networks are increasingly being used in a variety of machine
learning applications applied to rich user data on the cloud. However, this
approach introduces a number of privacy and efficiency challenges, as the cloud
operator can perform secondary inferences on the available data. Recently,
advances in edge processing have paved the way for more efficient, and private,
data processing at the source for simple tasks and lighter models, though they
remain a challenge for larger, and more complicated models. In this paper, we
present a hybrid approach for breaking down large, complex deep models for
cooperative, privacy-preserving analytics. We do this by breaking down the
popular deep architectures and fine-tune them in a particular way. We then
evaluate the privacy benefits of this approach based on the information exposed
to the cloud service. We also asses the local inference cost of different
layers on a modern handset for mobile applications. Our evaluations show that
by using certain kind of fine-tuning and embedding techniques and at a small
processing costs, we can greatly reduce the level of information available to
unintended tasks applied to the data feature on the cloud, and hence achieving
the desired tradeoff between privacy and performance.Comment: arXiv admin note: substantial text overlap with arXiv:1703.0295
Cloud-based Privacy Preserving Image Storage, Sharing and Search
High-resolution cameras produce huge volume of high quality images everyday.
It is extremely challenging to store, share and especially search those huge
images, for which increasing number of cloud services are presented to support
such functionalities. However, images tend to contain rich sensitive
information (\eg, people, location and event), and people's privacy concerns
hinder their readily participation into the services provided by untrusted
third parties. In this work, we introduce PIC: a Privacy-preserving large-scale
Image search system on Cloud. Our system enables efficient yet secure
content-based image search with fine-grained access control, and it also
provides privacy-preserving image storage and sharing among users. Users can
specify who can/cannot search on their images when using the system, and they
can search on others' images if they satisfy the condition specified by the
image owners. Majority of the computationally intensive jobs are outsourced to
the cloud side, and users only need to submit the query and receive the result
throughout the entire image search. Specially, to deal with massive images, we
design our system suitable for distributed and parallel computation and
introduce several optimizations to further expedite the search process. We
implement a prototype of PIC including both cloud side and client side. The
cloud side is a cluster of computers with distributed file system (Hadoop HDFS)
and MapReduce architecture (Hadoop MapReduce). The client side is built for
both Windows OS laptops and Android phones. We evaluate the prototype system
with large sets of real-life photos. Our security analysis and evaluation
results show that PIC successfully protect the image privacy at a low cost of
computation and communication.Comment: 15 pages, 12 figure
Reinforcement Learning Based Orchestration for Elastic Services
Due to the highly variable execution context in which edge services run,
adapting their behavior to the execution context is crucial to comply with
their requirements. However, adapting service behavior is a challenging task
because it is hard to anticipate the execution contexts in which it will be
deployed, as well as assessing the impact that each behavior change will
produce. In order to provide this adaptation efficiently, we propose a
Reinforcement Learning (RL) based Orchestration for Elastic Services. We
implement and evaluate this approach by adapting an elastic service in
different simulated execution contexts and comparing its performance to a
Heuristics based approach. We show that elastic services achieve high precision
and requirement satisfaction rates while creating an overhead of less than 0.5%
to the overall service. In particular, the RL approach proves to be more
efficient than its rule-based counterpart; yielding a 10 to 25% higher
precision while being 25% less computationally expensive.Comment: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 6 page
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur
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