41,820 research outputs found
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Privacy-Preserving Reengineering of Model-View-Controller Application Architectures Using Linked Data
When a legacy system’s software architecture cannot be redesigned, implementing
additional privacy requirements is often complex, unreliable and
costly to maintain. This paper presents a privacy-by-design approach to
reengineer web applications as linked data-enabled and implement access
control and privacy preservation properties. The method is based on the
knowledge of the application architecture, which for the Web of data is
commonly designed on the basis of a model-view-controller pattern. Whereas
wrapping techniques commonly used to link data of web applications duplicate
the security source code, the new approach allows for the controlled
disclosure of an application’s data, while preserving non-functional properties
such as privacy preservation. The solution has been implemented
and compared with existing linked data frameworks in terms of reliability,
maintainability and complexity
Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness
A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets
Publishing Microdata with a Robust Privacy Guarantee
Today, the publication of microdata poses a privacy threat. Vast research has
striven to define the privacy condition that microdata should satisfy before it
is released, and devise algorithms to anonymize the data so as to achieve this
condition. Yet, no method proposed to date explicitly bounds the percentage of
information an adversary gains after seeing the published data for each
sensitive value therein. This paper introduces beta-likeness, an appropriately
robust privacy model for microdata anonymization, along with two anonymization
schemes designed therefor, the one based on generalization, and the other based
on perturbation. Our model postulates that an adversary's confidence on the
likelihood of a certain sensitive-attribute (SA) value should not increase, in
relative difference terms, by more than a predefined threshold. Our techniques
aim to satisfy a given beta threshold with little information loss. We
experimentally demonstrate that (i) our model provides an effective privacy
guarantee in a way that predecessor models cannot, (ii) our generalization
scheme is more effective and efficient in its task than methods adapting
algorithms for the k-anonymity model, and (iii) our perturbation method
outperforms a baseline approach. Moreover, we discuss in detail the resistance
of our model and methods to attacks proposed in previous research.Comment: VLDB201
Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization
In privacy-preserving data publishing, approaches using Value Generalization
Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs
play a key role in the utility of published datasets as they dictate how the
anonymization of the data occurs. For categorical attributes, it is imperative
to preserve the semantics of the original data in order to achieve a higher
utility. Despite this, semantics have not being formally considered in the
specification of VGHs. Moreover, there are no methods that allow the users to
assess the quality of their VGH. In this paper, we propose a measurement
scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in
terms of semantic consistency and taxonomic organization, with the aim of
producing higher-quality anonymizations. We demonstrate, through a case study,
how our evaluation scheme can be used to compare the quality of multiple VGHs
and can help to identify faulty VGHs.Comment: 18 pages, 7 figures, presented in the Privacy in Statistical
Databases Conference 2014 (Ibiza, Spain
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