135 research outputs found
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of Location-Based Services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to Location-Based Services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users
Models and Mechanisms for Fairness in Location Data Processing
Location data use has become pervasive in the last decade due to the advent
of mobile apps, as well as novel areas such as smart health, smart cities, etc.
At the same time, significant concerns have surfaced with respect to fairness
in data processing. Individuals from certain population segments may be
unfairly treated when being considered for loan or job applications, access to
public resources, or other types of services. In the case of location data,
fairness is an important concern, given that an individual's whereabouts are
often correlated with sensitive attributes, e.g., race, income, education.
While fairness has received significant attention recently, e.g., in the case
of machine learning, there is little focus on the challenges of achieving
fairness when dealing with location data. Due to their characteristics and
specific type of processing algorithms, location data pose important fairness
challenges that must be addressed in a comprehensive and effective manner. In
this paper, we adapt existing fairness models to suit the specific properties
of location data and spatial processing. We focus on individual fairness, which
is more difficult to achieve, and more relevant for most location data
processing scenarios. First, we devise a novel building block to achieve
fairness in the form of fair polynomials. Then, we propose two mechanisms based
on fair polynomials that achieve individual fairness, corresponding to two
common interaction types based on location data. Extensive experimental results
on real data show that the proposed mechanisms achieve individual location
fairness without sacrificing utility
Privacy-preserving query transformation and processing in location based service
Ph.DDOCTOR OF PHILOSOPH
Protecting Locations with Differential Privacy under Temporal Correlations
Concerns on location privacy frequently arise with the rapid development of
GPS enabled devices and location-based applications. While spatial
transformation techniques such as location perturbation or generalization have
been studied extensively, most techniques rely on syntactic privacy models
without rigorous privacy guarantee. Many of them only consider static scenarios
or perturb the location at single timestamps without considering temporal
correlations of a moving user's locations, and hence are vulnerable to various
inference attacks. While differential privacy has been accepted as a standard
for privacy protection, applying differential privacy in location based
applications presents new challenges, as the protection needs to be enforced on
the fly for a single user and needs to incorporate temporal correlations
between a user's locations.
In this paper, we propose a systematic solution to preserve location privacy
with rigorous privacy guarantee. First, we propose a new definition,
"-location set" based differential privacy, to account for the temporal
correlations in location data. Second, we show that the well known
-norm sensitivity fails to capture the geometric sensitivity in
multidimensional space and propose a new notion, sensitivity hull, based on
which the error of differential privacy is bounded. Third, to obtain the
optimal utility we present a planar isotropic mechanism (PIM) for location
perturbation, which is the first mechanism achieving the lower bound of
differential privacy. Experiments on real-world datasets also demonstrate that
PIM significantly outperforms baseline approaches in data utility.Comment: Final version Nov-04-201
FUZZY MODELS AS DECISION-SUPPORT APPLICATIONS OF ELECTRICAL ENERGY TARIFFING
The paper is a decision – support application which design and use two fuzzy models to estimation an electrical energy tariff, as it to be sell at consumers. The fuzzy tariff estimation model integrate not only the S.C Electrica S.A. rate position, but and some constraints/ compulsions of National Authority of Settlements from Energy (NASE), beginning with 1999, in this transition period from Romania. The paper not refer to a price concrete case (internal tariff used in certain year, production price, transport price, distribution price, spot price, or an external price to be sold electrical energy – EE, etc). The paper shows how, by changing the parameters of S.C Electrica S.A and NASE, it is possible to can perform sensitivity tests on the tariff function model until we obtain an acceptable price. Much more: the two fuzzy models use different rules (conservative and aggressive, with hedge operators, respectively) for pricing. Finally, the paper not finished all fuzzy possibilities (rules) which can influences the expected value of a some EE tariff but, can create a discussion base about the way of approximate/ fuzzy reasoning, as a decision-support application to find a new EE price
De-anonymizable location cloaking for privacy-controlled mobile systems
The rapid technology upgrades of mobile devices and the popularity of wireless networks significantly drive the emergence and development of Location-based Services (LBSs), thus greatly expanding the business of online services and enriching the user experience. However, the personal location data shared with the service providers also leave hidden risks on location privacy. Location anonymization techniques transform the exact location of a user into a cloaking area by including the locations of multiple users in the exposed area such that the exposed location is indistinguishable from that of the other users. However in such schemes, location information once perturbed cannot be recovered from the cloaking region and as a result, users of the location cannot obtain fine granular information even when they have access to it. In this paper, we propose Dynamic Reversible Cloaking (DRC) a new de-anonymziable location cloaking mechanism that allows to restore the actual location from the perturbed information through the use of an anonymization key. Extensive experiments using realistic road network traces show that the proposed scheme is efficient, effective and scalable
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