21,170 research outputs found
Serving Online Requests with Mobile Servers
We study an online problem in which a set of mobile servers have to be moved
in order to efficiently serve a set of requests that arrive in an online
fashion. More formally, there is a set of nodes and a set of mobile
servers that are placed at some of the nodes. Each node can potentially host
several servers and the servers can be moved between the nodes. There are
requests that are adversarially issued at nodes one at a time. An
issued request at time needs to be served at all times . The
cost for serving the requests is a function of the number of servers and
requests at the different nodes. The requirements on how to serve the requests
are governed by two parameters and . An algorithm
needs to guarantee at all times that the total service cost remains within a
multiplicative factor of and an additive term of the current
optimal service cost. We consider online algorithms for two different
minimization objectives. We first consider the natural problem of minimizing
the total number of server movements. We show that in this case for every ,
the competitive ratio of every deterministic online algorithm needs to be at
least . Given this negative result, we then extend the minimization
objective to also include the current service cost. We give almost tight bounds
on the competitive ratio of the online problem where one needs to minimize the
sum of the total number of movements and the current service cost. In
particular, we show that at the cost of an additional additive term which is
roughly linear in , it is possible to achieve a multiplicative competitive
ratio of for every constant .Comment: 25 page
Communication-efficient Distributed Multi-resource Allocation
In several smart city applications, multiple resources must be allocated
among competing agents that are coupled through such shared resources and are
constrained --- either through limitations of communication infrastructure or
privacy considerations. We propose a distributed algorithm to solve such
distributed multi-resource allocation problems with no direct inter-agent
communication. We do so by extending a recently introduced additive-increase
multiplicative-decrease (AIMD) algorithm, which only uses very little
communication between the system and agents. Namely, a control unit broadcasts
a one-bit signal to agents whenever one of the allocated resources exceeds
capacity. Agents then respond to this signal in a probabilistic manner. In the
proposed algorithm, each agent makes decision of its resource demand locally
and an agent is unaware of the resource allocation of other agents. In
empirical results, we observe that the average allocations converge over time
to optimal allocations.Comment: To appear in IEEE International Smart Cities Conference (ISC2 2018),
Kansas City, USA, September, 2018. arXiv admin note: substantial text overlap
with arXiv:1711.0197
XONN: XNOR-based Oblivious Deep Neural Network Inference
Advancements in deep learning enable cloud servers to provide
inference-as-a-service for clients. In this scenario, clients send their raw
data to the server to run the deep learning model and send back the results.
One standing challenge in this setting is to ensure the privacy of the clients'
sensitive data. Oblivious inference is the task of running the neural network
on the client's input without disclosing the input or the result to the server.
This paper introduces XONN, a novel end-to-end framework based on Yao's Garbled
Circuits (GC) protocol, that provides a paradigm shift in the conceptual and
practical realization of oblivious inference. In XONN, the costly
matrix-multiplication operations of the deep learning model are replaced with
XNOR operations that are essentially free in GC. We further provide a novel
algorithm that customizes the neural network such that the runtime of the GC
protocol is minimized without sacrificing the inference accuracy.
We design a user-friendly high-level API for XONN, allowing expression of the
deep learning model architecture in an unprecedented level of abstraction.
Extensive proof-of-concept evaluation on various neural network architectures
demonstrates that XONN outperforms prior art such as Gazelle (USENIX
Security'18) by up to 7x, MiniONN (ACM CCS'17) by 93x, and SecureML (IEEE
S&P'17) by 37x. State-of-the-art frameworks require one round of interaction
between the client and the server for each layer of the neural network,
whereas, XONN requires a constant round of interactions for any number of
layers in the model. XONN is first to perform oblivious inference on Fitnet
architectures with up to 21 layers, suggesting a new level of scalability
compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to
perform privacy-preserving medical diagnosis.Comment: To appear in USENIX Security 201
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