18,280 research outputs found
Emerging privacy challenges and approaches in CAV systems
The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions
Privacy-Preserving Vehicle Assignment for Mobility-on-Demand Systems
Urban transportation is being transformed by mobility-on-demand (MoD)
systems. One of the goals of MoD systems is to provide personalized
transportation services to passengers. This process is facilitated by a
centralized operator that coordinates the assignment of vehicles to individual
passengers, based on location data. However, current approaches assume that
accurate positioning information for passengers and vehicles is readily
available. This assumption raises privacy concerns. In this work, we address
this issue by proposing a method that protects passengers' drop-off locations
(i.e., their travel destinations). Formally, we solve a batch assignment
problem that routes vehicles at obfuscated origin locations to passenger
locations (since origin locations correspond to previous drop-off locations),
such that the mean waiting time is minimized. Our main contributions are
two-fold. First, we formalize the notion of privacy for continuous
vehicle-to-passenger assignment in MoD systems, and integrate a privacy
mechanism that provides formal guarantees. Second, we present a scalable
algorithm that takes advantage of superfluous (idle) vehicles in the system,
combining multiple iterations of the Hungarian algorithm to allocate a
redundant number of vehicles to a single passenger. As a result, we are able to
reduce the performance deterioration induced by the privacy mechanism. We
evaluate our methods on a real, large-scale data set consisting of over 11
million taxi rides (specifying vehicle availability and passenger requests),
recorded over a month's duration, in the area of Manhattan, New York. Our work
demonstrates that privacy can be integrated into MoD systems without incurring
a significant loss of performance, and moreover, that this loss can be further
minimized at the cost of deploying additional (redundant) vehicles into the
fleet.Comment: 8 pages; Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), 201
A Survey on Wireless Sensor Network Security
Wireless sensor networks (WSNs) have recently attracted a lot of interest in
the research community due their wide range of applications. Due to distributed
nature of these networks and their deployment in remote areas, these networks
are vulnerable to numerous security threats that can adversely affect their
proper functioning. This problem is more critical if the network is deployed
for some mission-critical applications such as in a tactical battlefield.
Random failure of nodes is also very likely in real-life deployment scenarios.
Due to resource constraints in the sensor nodes, traditional security
mechanisms with large overhead of computation and communication are infeasible
in WSNs. Security in sensor networks is, therefore, a particularly challenging
task. This paper discusses the current state of the art in security mechanisms
for WSNs. Various types of attacks are discussed and their countermeasures
presented. A brief discussion on the future direction of research in WSN
security is also included.Comment: 24 pages, 4 figures, 2 table
Privacy Management and Optimal Pricing in People-Centric Sensing
With the emerging sensing technologies such as mobile crowdsensing and
Internet of Things (IoT), people-centric data can be efficiently collected and
used for analytics and optimization purposes. This data is typically required
to develop and render people-centric services. In this paper, we address the
privacy implication, optimal pricing, and bundling of people-centric services.
We first define the inverse correlation between the service quality and privacy
level from data analytics perspectives. We then present the profit maximization
models of selling standalone, complementary, and substitute services.
Specifically, the closed-form solutions of the optimal privacy level and
subscription fee are derived to maximize the gross profit of service providers.
For interrelated people-centric services, we show that cooperation by service
bundling of complementary services is profitable compared to the separate sales
but detrimental for substitutes. We also show that the market value of a
service bundle is correlated with the degree of contingency between the
interrelated services. Finally, we incorporate the profit sharing models from
game theory for dividing the bundling profit among the cooperative service
providers.Comment: 16 page
- …