4,244 research outputs found
Optimizing Locally Differentially Private Protocols
Protocols satisfying Local Differential Privacy (LDP) enable parties to
collect aggregate information about a population while protecting each user's
privacy, without relying on a trusted third party. LDP protocols (such as
Google's RAPPOR) have been deployed in real-world scenarios. In these
protocols, a user encodes his private information and perturbs the encoded
value locally before sending it to an aggregator, who combines values that
users contribute to infer statistics about the population. In this paper, we
introduce a framework that generalizes several LDP protocols proposed in the
literature. Our framework yields a simple and fast aggregation algorithm, whose
accuracy can be precisely analyzed. Our in-depth analysis enables us to choose
optimal parameters, resulting in two new protocols (i.e., Optimized Unary
Encoding and Optimized Local Hashing) that provide better utility than
protocols previously proposed. We present precise conditions for when each
proposed protocol should be used, and perform experiments that demonstrate the
advantage of our proposed protocols
Towards Extending Noiseless Privacy -- Dependent Data and More Practical Approach
In 2011 Bhaskar et al. pointed out that in many cases one can ensure
sufficient level of privacy without adding noise by utilizing adversarial
uncertainty. Informally speaking, this observation comes from the fact that if
at least a part of the data is randomized from the adversary's point of view,
it can be effectively used for hiding other values. So far the approach to that
idea in the literature was mostly purely asymptotic, which greatly limited its
adaptation in real-life scenarios. In this paper we aim to make the concept of
utilizing adversarial uncertainty not only an interesting theoretical idea, but
rather a practically useful technique, complementary to differential privacy,
which is the state-of-the-art definition of privacy. This requires
non-asymptotic privacy guarantees, more realistic approach to the randomness
inherently present in the data and to the adversary's knowledge. In our paper
we extend the concept proposed by Bhaskar et al. and present some results for
wider class of data. In particular we cover the data sets that are dependent.
We also introduce rigorous adversarial model. Moreover, in contrast to most of
previous papers in this field, we give detailed (non-asymptotic) results which
is motivated by practical reasons. Note that it required a modified approach
and more subtle mathematical tools, including Stein method which, to the best
of our knowledge, was not used in privacy research before. Apart from that, we
show how to combine adversarial uncertainty with differential privacy approach
and explore synergy between them to enhance the privacy parameters already
present in the data itself by adding small amount of noise.Comment: Accepted to AsiaCCS 201
Security and Privacy Issues in Deep Learning
With the development of machine learning (ML), expectations for artificial
intelligence (AI) technology have been increasing daily. In particular, deep
neural networks have shown outstanding performance results in many fields. Many
applications are deeply involved in our daily life, such as making significant
decisions in application areas based on predictions or classifications, in
which a DL model could be relevant. Hence, if a DL model causes mispredictions
or misclassifications due to malicious external influences, then it can cause
very large difficulties in real life. Moreover, training DL models involve an
enormous amount of data and the training data often include sensitive
information. Therefore, DL models should not expose the privacy of such data.
In this paper, we review the vulnerabilities and the developed defense methods
on the security of the models and data privacy under the notion of secure and
private AI (SPAI). We also discuss current challenges and open issues
Computational Differential Privacy from Lattice-based Cryptography
The emerging technologies for large scale data analysis raise new challenges
to the security and privacy of sensitive user data. In this work we investigate
the problem of private statistical analysis of time-series data in the
distributed and semi-honest setting. In particular, we study some properties of
Private Stream Aggregation (PSA), first introduced by Shi et al. 2017. This is
a computationally secure protocol for the collection and aggregation of data in
a distributed network and has a very small communication cost. In the
non-adaptive query model, a secure PSA scheme can be built upon any
key-homomorphic weak pseudo-random function as shown by Valovich 2017, yielding
security guarantees in the standard model which is in contrast to Shi et. al.
We show that every mechanism which preserves -differential
privacy in effect preserves computational -differential
privacy when it is executed through a secure PSA scheme. Furthermore, we
introduce a novel perturbation mechanism based on the symmetric Skellam
distribution that is suited for preserving differential privacy in the
distributed setting, and find that its performances in terms of privacy and
accuracy are comparable to those of previous solutions. On the other hand, we
leverage its specific properties to construct a computationally efficient
prospective post-quantum protocol for differentially private time-series data
analysis in the distributed model. The security of this protocol is based on
the hardness of a new variant of the Decisional Learning with Errors (DLWE)
problem. In this variant the errors are taken from the symmetric Skellam
distribution. We show that this new variant is hard based on the hardness of
the standard Learning with Errors (LWE) problem where the errors are taken from
the discrete Gaussian distribution. Thus, we provide a variant of the LWE
problem that is hard...Comment: arXiv admin note: substantial text overlap with arXiv:1507.0807
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Active learning holds promise of significantly reducing data annotation costs
while maintaining reasonable model performance. However, it requires sending
data to annotators for labeling. This presents a possible privacy leak when the
training set includes sensitive user data. In this paper, we describe an
approach for carrying out privacy preserving active learning with quantifiable
guarantees. We evaluate our approach by showing the tradeoff between privacy,
utility and annotation budget on a binary classification task in a active
learning setting.Comment: To appear at PAL: Privacy-Enhancing Artificial Intelligence and
Language Technologies as part of the AAAI Spring Symposium Series (AAAI-SSS
2019
An Efficient Fog-Assisted Unstable Sensor Detection Scheme with Privacy Preserved
The Internet of Thing (IoT) has been a hot topic in both research community
and industry. It is anticipated that in future IoT, an enormous number of
sensors will collect the physical information every moment to enable the
control center making better decisions to improve the quality of service (QoS).
However, the sensors maybe faulty and thus generate inaccurate data which would
compromise the decision making. To guarantee the QoS, the system should be able
to detect faulty sensors so as to eliminate the damages of inaccurate data.
Various faulty sensor detection mechanisms have been developed in the context
of wireless sensor network (WSN). Some of them are only fit for WSN while the
others would bring a communication burden to control center. To detect the
faulty sensors for general IoT applications and save the communication resource
at the same time, an efficient faulty sensor detection scheme is proposed in
this paper. The proposed scheme takes advantage of fog computing to save the
computation and communication resource of control center. To preserve the
privacy of sensor data, the Paillier Cryptosystem is adopted in the fog
computing. The batch verification technique is applied to achieve efficient
authentication. The performance analyses are presented to demonstrate that the
proposed detection scheme is able to conserve the communication resource of
control center and achieve a high true positive ratio while maintaining an
acceptable false positive ratio. The scheme could also withstand various
security attacks and preserve data privacy.Comment: 11 pages, 5 figure
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
Price of Privacy in the Keynesian Beauty Contest
The Keynesian Beauty Contest is a classical game in which strategic agents
seek to both accurately guess the true state of the world as well as the
average action of all agents. We study an augmentation of this game where
agents are concerned about revealing their private information and additionally
suffer a loss based on how well an observer can infer their private signals. We
solve for an equilibrium of this augmented game and quantify the loss of social
welfare as a result of agents acting to obscure their private information,
which we call the 'price of privacy'. We analyze two versions of this this
price: one from the perspective of the agents measuring their diminished
ability to coordinate due to acting to obscure their information and another
from the perspective of an aggregator whose statistical estimate of the true
state of the world is of lower precision due to the agents adding random noise
to their actions. We show that these quantities are high when agents care very
strongly about protecting their personal information and low when the quality
of the signals the agents receive is poor.Comment: 26 page
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Knowledge Transferring via Model Aggregation for Online Social Care
The Internet and the Web are being increasingly used in proactive social care
to provide people, especially the vulnerable, with a better life and services,
and their derived social services generate enormous data. However, the strict
protection of privacy makes user's data become an isolated island and limits
the predictive performance of standalone clients. To enable effective proactive
social care and knowledge sharing within intelligent agents, this paper
develops a knowledge transferring framework via model aggregation. Under this
framework, distributed clients perform on-device training, and a third-party
server integrates multiple clients' models and redistributes to clients for
knowledge transferring among users. To improve the generalizability of the
knowledge sharing, we further propose a novel model aggregation algorithm,
namely the average difference descent aggregation (AvgDiffAgg for short). In
particular, to evaluate the effectiveness of the learning algorithm, we use a
case study on the early detection and prevention of suicidal ideation, and the
experiment results on four datasets derived from social communities demonstrate
the effectiveness of the proposed learning method
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