83,865 research outputs found
Innovative public governance through cloud computing: Information privacy, business models and performance measurement challenges
Purpose: The purpose of this paper is to identify and analyze challenges and to discuss proposed solutions for innovative public governance through cloud computing. Innovative technologies, such as federation of services and cloud computing, can greatly contribute to the provision of e-government services, through scaleable and flexible systems. Furthermore, they can facilitate in reducing costs and overcoming public information segmentation. Nonetheless, when public agencies use these technologies, they encounter several associated organizational and technical changes, as well as significant challenges. Design/methodology/approach: We followed a multidisciplinary perspective (social, behavioral, business and technical) and conducted a conceptual analysis for analyzing the associated challenges. We conducted focus group interviews in two countries for evaluating the performance models that resulted from the conceptual analysis. Findings: This study identifies and analyzes several challenges that may emerge while adopting innovative technologies for public governance and e-government services. Furthermore, it presents suggested solutions deriving from the experience of designing a related platform for public governance, including issues of privacy requirements, proposed business models and key performance indicators for public services on cloud computing. Research limitations/implications: The challenges and solutions discussed are based on the experience gained by designing one platform. However, we rely on issues and challenges collected from four countries. Practical implications: The identification of challenges for innovative design of e-government services through a central portal in Europe and using service federation is expected to inform practitioners in different roles about significant changes across multiple levels that are implied and may accelerate the challenges' resolution. Originality/value: This is the first study that discusses from multiple perspectives and through empirical investigation the challenges to realize public governance through innovative technologies. The results emerge from an actual portal that will function at a European level. © Emerald Group Publishing Limited
Measuring third party tracker power across web and mobile
Third-party networks collect vast amounts of data about users via web sites
and mobile applications. Consolidations among tracker companies can
significantly increase their individual tracking capabilities, prompting
scrutiny by competition regulators. Traditional measures of market share, based
on revenue or sales, fail to represent the tracking capability of a tracker,
especially if it spans both web and mobile. This paper proposes a new approach
to measure the concentration of tracking capability, based on the reach of a
tracker on popular websites and apps. Our results reveal that tracker
prominence and parent-subsidiary relationships have significant impact on
accurately measuring concentration
On the anonymity risk of time-varying user profiles.
Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users’ preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends’ activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.Peer ReviewedPostprint (published version
Survey of End-to-End Mobile Network Measurement Testbeds, Tools, and Services
Mobile (cellular) networks enable innovation, but can also stifle it and lead
to user frustration when network performance falls below expectations. As
mobile networks become the predominant method of Internet access, developer,
research, network operator, and regulatory communities have taken an increased
interest in measuring end-to-end mobile network performance to, among other
goals, minimize negative impact on application responsiveness. In this survey
we examine current approaches to end-to-end mobile network performance
measurement, diagnosis, and application prototyping. We compare available tools
and their shortcomings with respect to the needs of researchers, developers,
regulators, and the public. We intend for this survey to provide a
comprehensive view of currently active efforts and some auspicious directions
for future work in mobile network measurement and mobile application
performance evaluation.Comment: Submitted to IEEE Communications Surveys and Tutorials. arXiv does
not format the URL references correctly. For a correctly formatted version of
this paper go to
http://www.cs.montana.edu/mwittie/publications/Goel14Survey.pd
You never surf alone. Ubiquitous tracking of users' browsing habits
In the early age of the internet users enjoyed a large level of anonymity. At
the time web pages were just hypertext documents; almost no personalisation of
the user experience was o ered. The Web today has evolved as a world wide
distributed system following specific architectural paradigms. On the web now,
an enormous quantity of user generated data is shared and consumed by a network
of applications and services, reasoning upon users expressed preferences and
their social and physical connections. Advertising networks follow users'
browsing habits while they surf the web, continuously collecting their traces
and surfing patterns. We analyse how users tracking happens on the web by
measuring their online footprint and estimating how quickly advertising
networks are able to pro le users by their browsing habits
CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions
By regularly querying Web search engines, users (unconsciously) disclose
large amounts of their personal data as part of their search queries, among
which some might reveal sensitive information (e.g. health issues, sexual,
political or religious preferences). Several solutions exist to allow users
querying search engines while improving privacy protection. However, these
solutions suffer from a number of limitations: some are subject to user
re-identification attacks, while others lack scalability or are unable to
provide accurate results. This paper presents CYCLOSA, a secure, scalable and
accurate private Web search solution. CYCLOSA improves security by relying on
trusted execution environments (TEEs) as provided by Intel SGX. Further,
CYCLOSA proposes a novel adaptive privacy protection solution that reduces the
risk of user re- identification. CYCLOSA sends fake queries to the search
engine and dynamically adapts their count according to the sensitivity of the
user query. In addition, CYCLOSA meets scalability as it is fully
decentralized, spreading the load for distributing fake queries among other
nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real
query and the fake queries separately, in contrast to other existing solutions
that mix fake and real query results
On Web User Tracking: How Third-Party Http Requests Track Users' Browsing Patterns for Personalised Advertising
On today's Web, users trade access to their private data for content and
services. Advertising sustains the business model of many websites and
applications. Efficient and successful advertising relies on predicting users'
actions and tastes to suggest a range of products to buy. It follows that,
while surfing the Web users leave traces regarding their identity in the form
of activity patterns and unstructured data. We analyse how advertising networks
build user footprints and how the suggested advertising reacts to changes in
the user behaviour.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0653
Third Party Tracking in the Mobile Ecosystem
Third party tracking allows companies to identify users and track their
behaviour across multiple digital services. This paper presents an empirical
study of the prevalence of third-party trackers on 959,000 apps from the US and
UK Google Play stores. We find that most apps contain third party tracking, and
the distribution of trackers is long-tailed with several highly dominant
trackers accounting for a large portion of the coverage. The extent of tracking
also differs between categories of apps; in particular, news apps and apps
targeted at children appear to be amongst the worst in terms of the number of
third party trackers associated with them. Third party tracking is also
revealed to be a highly trans-national phenomenon, with many trackers operating
in jurisdictions outside the EU. Based on these findings, we draw out some
significant legal compliance challenges facing the tracking industry.Comment: Corrected missing company info (Linkedin owned by Microsoft). Figures
for Microsoft and Linkedin re-calculated and added to Table
XRay: Enhancing the Web's Transparency with Differential Correlation
Today's Web services - such as Google, Amazon, and Facebook - leverage user
data for varied purposes, including personalizing recommendations, targeting
advertisements, and adjusting prices. At present, users have little insight
into how their data is being used. Hence, they cannot make informed choices
about the services they choose. To increase transparency, we developed XRay,
the first fine-grained, robust, and scalable personal data tracking system for
the Web. XRay predicts which data in an arbitrary Web account (such as emails,
searches, or viewed products) is being used to target which outputs (such as
ads, recommended products, or prices). XRay's core functions are service
agnostic and easy to instantiate for new services, and they can track data
within and across services. To make predictions independent of the audited
service, XRay relies on the following insight: by comparing outputs from
different accounts with similar, but not identical, subsets of data, one can
pinpoint targeting through correlation. We show both theoretically, and through
experiments on Gmail, Amazon, and YouTube, that XRay achieves high precision
and recall by correlating data from a surprisingly small number of extra
accounts.Comment: Extended version of a paper presented at the 23rd USENIX Security
Symposium (USENIX Security 14
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