6,122 research outputs found
Securing the Participation of Safety-Critical SCADA Systems in the Industrial Internet of Things
In the past, industrial control systems were ‘air gapped’ and
isolated from more conventional networks. They used
specialist protocols, such as Modbus, that are very different
from TCP/IP. Individual devices used proprietary operating
systems rather than the more familiar Linux or Windows.
However, things are changing. There is a move for greater
connectivity – for instance so that higher-level enterprise
management systems can exchange information that helps
optimise production processes. At the same time, industrial
systems have been influenced by concepts from the Internet
of Things; where the information derived from sensors and
actuators in domestic and industrial components can be
addressed through network interfaces. This paper identifies a
range of cyber security and safety concerns that arise from
these developments. The closing sections introduce potential
solutions and identify areas for future research
Contributions to privacy in web search engines
Els motors de cerca d’Internet recullen i emmagatzemen informació sobre els seus usuaris per tal d’oferir-los millors serveis. A canvi de rebre un servei personalitzat, els usuaris perden el control de les seves pròpies dades. Els registres de cerca poden revelar informació sensible de l’usuari, o fins i tot revelar la seva identitat. En aquesta tesis tractem com limitar aquests problemes de privadesa mentre mantenim suficient informació a les dades.
La primera part d’aquesta tesis tracta els mètodes per prevenir la recollida d’informació per part dels motores de cerca. Ja que aquesta informació es requerida per oferir un servei precÃs, l’objectiu es proporcionar registres de cerca que siguin adequats per proporcionar personalització. Amb aquesta finalitat, proposem un protocol que empra una xarxa social per tal d’ofuscar els perfils dels usuaris.
La segona part tracta la disseminació de registres de cerca. Proposem tècniques que la permeten, proporcionant k-anonimat i minimitzant la pèrdua d’informació.Web Search Engines collects and stores information about their users in order to tailor their services better to their users' needs. Nevertheless, while receiving a personalized attention, the users lose the control over their own data. Search logs can disclose sensitive information and the identities of the users, creating risks of privacy breaches. In this thesis we discuss the problem of limiting the disclosure risks while minimizing the information loss.
The first part of this thesis focuses on the methods to prevent the gathering of information by WSEs. Since search logs are needed in order to receive an accurate service, the aim is to provide logs that are still suitable to provide personalization. We propose a protocol which uses a social network to obfuscate users' profiles.
The second part deals with the dissemination of search logs. We propose microaggregation techniques which allow the publication of search logs, providing -anonymity while minimizing the information loss
An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices
Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy
Econometrics meets sentiment : an overview of methodology and applications
The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software
Semantic-Based Policy Composition for Privacy-Demanding Data Linkage
Record linkage can be used to support current and future health research across populations however such approaches give rise to many challenges related to patient privacy and confidentiality including inference attacks. To address this, we present a semantic-based policy framework where linkage privacy detects attribute associations that can lead to inference disclosure issues. To illustrate the effectiveness of the approach, we present a case study exploring health data combining spatial, ethnicity and language information from several major on-going projects occurring across Australia. Compared with classic access control models, the results show that our proposal outperforms other approaches with regards to effectiveness, reliability and subsequent data utility
Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
The growing expanse of e-commerce and the widespread availability of online
databases raise many fears regarding loss of privacy and many statistical
challenges. Even with encryption and other nominal forms of protection for
individual databases, we still need to protect against the violation of privacy
through linkages across multiple databases. These issues parallel those that
have arisen and received some attention in the context of homeland security.
Following the events of September 11, 2001, there has been heightened attention
in the United States and elsewhere to the use of multiple government and
private databases for the identification of possible perpetrators of future
attacks, as well as an unprecedented expansion of federal government data
mining activities, many involving databases containing personal information. We
present an overview of some proposals that have surfaced for the search of
multiple databases which supposedly do not compromise possible pledges of
confidentiality to the individuals whose data are included. We also explore
their link to the related literature on privacy-preserving data mining. In
particular, we focus on the matching problem across databases and the concept
of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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
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