3,993 research outputs found
C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics.
The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is tampered with from the cloud or a hospital due to third-party storage services. Most of the existing systems use an owner-centric data integrity verification mechanism, which is not computationally feasible for lightweight wearable-sensor systems because of limited computing capacity and privacy leakage issues. In this regard, we design a 2-step Privacy-Preserving Multidimensional Data Stream (PPMDS) approach based on a cloudlet framework with an Uncertain Data-integrity Optimization (UDO) model and Sparse-Centric SVM (SCS) model. The UDO model enhances health data security with an adaptive cryptosystem called Cloudlet-Nonsquare Encryption Secret Transmission (C-NEST) strategy by avoiding medical disputes during data streaming based on novel signature and key generation strategies. The SCS model effectively classifies incoming queries for easy access to data by solving scalability issues. The cloudlet server measures data integrity and authentication factors to optimize third-party verification burden and computational cost. The simulation outcomes show that the proposed system optimizes average data leakage error rate by 27%, query response time and average data transmission time are reduced by 31%, and average communication-computation cost are reduced by 61% when measured against state-of-the-art approaches
Contributions to the privacy provisioning for federated identity management platforms
Identity information, personal data and user’s profiles are key assets for organizations
and companies by becoming the use of identity management (IdM) infrastructures a prerequisite
for most companies, since IdM systems allow them to perform their business
transactions by sharing information and customizing services for several purposes in more
efficient and effective ways.
Due to the importance of the identity management paradigm, a lot of work has been done
so far resulting in a set of standards and specifications. According to them, under the
umbrella of the IdM paradigm a person’s digital identity can be shared, linked and reused
across different domains by allowing users simple session management, etc. In this way,
users’ information is widely collected and distributed to offer new added value services
and to enhance availability. Whereas these new services have a positive impact on users’
life, they also bring privacy problems.
To manage users’ personal data, while protecting their privacy, IdM systems are the ideal
target where to deploy privacy solutions, since they handle users’ attribute exchange.
Nevertheless, current IdM models and specifications do not sufficiently address comprehensive
privacy mechanisms or guidelines, which enable users to better control over the
use, divulging and revocation of their online identities. These are essential aspects, specially
in sensitive environments where incorrect and unsecured management of user’s data
may lead to attacks, privacy breaches, identity misuse or frauds.
Nowadays there are several approaches to IdM that have benefits and shortcomings, from
the privacy perspective.
In this thesis, the main goal is contributing to the privacy provisioning for federated
identity management platforms. And for this purpose, we propose a generic architecture
that extends current federation IdM systems. We have mainly focused our contributions
on health care environments, given their particularly sensitive nature. The two main
pillars of the proposed architecture, are the introduction of a selective privacy-enhanced
user profile management model and flexibility in revocation consent by incorporating an
event-based hybrid IdM approach, which enables to replace time constraints and explicit
revocation by activating and deactivating authorization rights according to events. The
combination of both models enables to deal with both online and offline scenarios, as well
as to empower the user role, by letting her to bring together identity information from
different sources.
Regarding user’s consent revocation, we propose an implicit revocation consent mechanism
based on events, that empowers a new concept, the sleepyhead credentials, which
is issued only once and would be used any time. Moreover, we integrate this concept
in IdM systems supporting a delegation protocol and we contribute with the definition
of mathematical model to determine event arrivals to the IdM system and how they are
managed to the corresponding entities, as well as its integration with the most widely
deployed specification, i.e., Security Assertion Markup Language (SAML).
In regard to user profile management, we define a privacy-awareness user profile management
model to provide efficient selective information disclosure. With this contribution a
service provider would be able to accesses the specific personal information without being
able to inspect any other details and keeping user control of her data by controlling
who can access. The structure that we consider for the user profile storage is based on
extensions of Merkle trees allowing for hash combining that would minimize the need of
individual verification of elements along a path. An algorithm for sorting the tree as we
envision frequently accessed attributes to be closer to the root (minimizing the access’
time) is also provided.
Formal validation of the above mentioned ideas has been carried out through simulations
and the development of prototypes. Besides, dissemination activities were performed in
projects, journals and conferences.Programa Oficial de Doctorado en IngenierĂa TelemáticaPresidente: MarĂa Celeste Campo Vázquez.- Secretario: MarĂa Francisca Hinarejos Campos.- Vocal: Ă“scar Esparza MartĂ
A Middleware for the Internet of Things
The Internet of Things (IoT) connects everyday objects including a vast array
of sensors, actuators, and smart devices, referred to as things to the
Internet, in an intelligent and pervasive fashion. This connectivity gives rise
to the possibility of using the tracking capabilities of things to impinge on
the location privacy of users. Most of the existing management and location
privacy protection solutions do not consider the low-cost and low-power
requirements of things, or, they do not account for the heterogeneity,
scalability, or autonomy of communications supported in the IoT. Moreover,
these traditional solutions do not consider the case where a user wishes to
control the granularity of the disclosed information based on the context of
their use (e.g. based on the time or the current location of the user). To fill
this gap, a middleware, referred to as the Internet of Things Management
Platform (IoT-MP) is proposed in this paper.Comment: 20 pages, International Journal of Computer Networks & Communications
(IJCNC) Vol.8, No.2, March 201
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