7,773 research outputs found
Search Me If You Can: Privacy-preserving Location Query Service
Location-Based Service (LBS) becomes increasingly popular with the dramatic
growth of smartphones and social network services (SNS), and its context-rich
functionalities attract considerable users. Many LBS providers use users'
location information to offer them convenience and useful functions. However,
the LBS could greatly breach personal privacy because location itself contains
much information. Hence, preserving location privacy while achieving utility
from it is still an challenging question now. This paper tackles this
non-trivial challenge by designing a suite of novel fine-grained
Privacy-preserving Location Query Protocol (PLQP). Our protocol allows
different levels of location query on encrypted location information for
different users, and it is efficient enough to be applied in mobile platforms.Comment: 9 pages, 1 figure, 2 tables, IEEE INFOCOM 201
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
Time Distortion Anonymization for the Publication of Mobility Data with High Utility
An increasing amount of mobility data is being collected every day by
different means, such as mobile applications or crowd-sensing campaigns. This
data is sometimes published after the application of simple anonymization
techniques (e.g., putting an identifier instead of the users' names), which
might lead to severe threats to the privacy of the participating users.
Literature contains more sophisticated anonymization techniques, often based on
adding noise to the spatial data. However, these techniques either compromise
the privacy if the added noise is too little or the utility of the data if the
added noise is too strong. We investigate in this paper an alternative
solution, which builds on time distortion instead of spatial distortion.
Specifically, our contribution lies in (1) the introduction of the concept of
time distortion to anonymize mobility datasets (2) Promesse, a protection
mechanism implementing this concept (3) a practical study of Promesse compared
to two representative spatial distortion mechanisms, namely Wait For Me, which
enforces k-anonymity, and Geo-Indistinguishability, which enforces differential
privacy. We evaluate our mechanism practically using three real-life datasets.
Our results show that time distortion reduces the number of points of interest
that can be retrieved by an adversary to under 3 %, while the introduced
spatial error is almost null and the distortion introduced on the results of
range queries is kept under 13 % on average.Comment: in 14th IEEE International Conference on Trust, Security and Privacy
in Computing and Communications, Aug 2015, Helsinki, Finlan
Location Privacy in Spatial Crowdsourcing
Spatial crowdsourcing (SC) is a new platform that engages individuals in
collecting and analyzing environmental, social and other spatiotemporal
information. With SC, requesters outsource their spatiotemporal tasks to a set
of workers, who will perform the tasks by physically traveling to the tasks'
locations. This chapter identifies privacy threats toward both workers and
requesters during the two main phases of spatial crowdsourcing, tasking and
reporting. Tasking is the process of identifying which tasks should be assigned
to which workers. This process is handled by a spatial crowdsourcing server
(SC-server). The latter phase is reporting, in which workers travel to the
tasks' locations, complete the tasks and upload their reports to the SC-server.
The challenge is to enable effective and efficient tasking as well as reporting
in SC without disclosing the actual locations of workers (at least until they
agree to perform a task) and the tasks themselves (at least to workers who are
not assigned to those tasks). This chapter aims to provide an overview of the
state-of-the-art in protecting users' location privacy in spatial
crowdsourcing. We provide a comparative study of a diverse set of solutions in
terms of task publishing modes (push vs. pull), problem focuses (tasking and
reporting), threats (server, requester and worker), and underlying technical
approaches (from pseudonymity, cloaking, and perturbation to exchange-based and
encryption-based techniques). The strengths and drawbacks of the techniques are
highlighted, leading to a discussion of open problems and future work
On content-based recommendation and user privacy in social-tagging systems
Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft
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