scholarly journals No boundaries: data exfiltration by third parties embedded on web pages

2020 ◽  
Vol 2020 (4) ◽  
pp. 220-238
Author(s):  
Gunes Acar ◽  
Steven Englehardt ◽  
Arvind Narayanan

AbstractWe investigate data exfiltration by third-party scripts directly embedded on web pages. Specifically, we study three attacks: misuse of browsers’ internal login managers, social data exfiltration, and whole-DOM exfiltration. Although the possibility of these attacks was well known, we provide the first empirical evidence based on measurements of 300,000 distinct web pages from 50,000 sites. We extend OpenWPM’s instrumentation to detect and precisely attribute these attacks to specific third-party scripts. Our analysis reveals invasive practices such as inserting invisible login forms to trigger autofilling of the saved user credentials, and reading and exfiltrating social network data when the user logs in via Facebook login. Further, we uncovered password, credit card, and health data leaks to third parties due to wholesale collection of the DOM. We discuss the lessons learned from the responses to the initial disclosure of our findings and fixes that were deployed by the websites, browser vendors, third-party libraries and privacy protection tools.

Author(s):  
Heike Neumann ◽  
Thomas Schwarzpaul

For real-world applications digital coin systems, i.e., off-line payment systems offering not only the unforgeability of conventional coins but also the anonymity of customers making purchases, need to have some additional features. One of these additional features is the hardware protection of the system, provided by dedicated tamper-resistant devices called observers which are used to physically prevent illegitimate copying of coins. Another essential feature for practical applications is anonymity-revocation mechanisms. Basically, digital coin systems guarantee perfect anonymity, i.e., the bank cannot link views of the withdrawal and payments in order to determine whether or not a customer spent her money at a certain shop. The customer’s privacy protection is the main difference between coin systems and those based on credit card or cheque based systems. While privacy protection is a desirable property of cash systems, perfect anonymity is not. Perfect anonymity makes possible perfect blackmailing or money laundering. To prevent such “perfect crime” it must be possible to revoke the anonymity of customers in case of need. Anonymity revocation is done by a trusted third party. Cash systems that allow anonymity revocation are called fair. We present a coin system featuring both observer and fairness, showing that both concepts do not interfere with each other and can be implemented simultaneously without loss of security. We prove this claim not only by presenting a fair variant of the Brands’ coin system but additionally by outlining a generic framework for fair wallets in which essentially any blind signature scheme can be used. Unlike other fair off-line coin systems, fairness is implemented with the help of the observer, thereby reducing the computational effort during the withdrawal.


2012 ◽  
Vol E95-D (1) ◽  
pp. 152-160
Author(s):  
Min Kyoung SUNG ◽  
Ki Yong LEE ◽  
Jun-Bum SHIN ◽  
Yon Dohn CHUNG

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0130693 ◽  
Author(s):  
Mehri Rajaei ◽  
Mostafa S. Haghjoo ◽  
Eynollah Khanjari Miyaneh

2008 ◽  
Vol 112 (1130) ◽  
pp. 207-212
Author(s):  
R. Harris

Abstract Successful, seamless interchange of simulation databases has long proved surprisingly difficult to achieve. Numerous technical difficulties, arising from the different environmental representations used by different simulation systems, have proved to be only one facet of this difficulty. Often such problems are in fact the result of more fundamental underlying issues, such as the mathematical relationships between different co-ordinate systems. Logistical issues, and collaborative aspects of database interchange between different groups or companies, also contribute to the problems. Thales has encountered many of these issues over the years in generating a range of databases for its simulation systems. These databases are required to correlate closely with other sensor systems, in particular the visual, but it is often the case that these other systems are third party products, using databases modelled by companies other than Thales. In these circumstances, the strategy used by Thales to generate its databases has typically been to derive them directly from the visual database. This has involved directly processing the visual database, extracting relevant geometry and attribution and formatting it for use by the Thales simulation systems. Historically, such visual databases have been provided by third parties using the SIF/HDI interchange format and imported directly into the Thales database generation toolset. While generating such derived databases in this way has been achieved successfully, many interchange issues referred to above were encountered and needed to be addressed. When the need arose to replace SIF/HDI, the opportunity was taken to seek a replacement that would not only provide better representational capabilities but also address many of the wider, non-technical issues as well. Analysis of a variety of formats was undertaken and SEDRIS emerged as by far the strongest contender. Not only did it provide the best all round support for existing data representation requirements, it also gave good support for addressing wider interchange issues and offered a variety of opportunities to enhance the database generation toolset, both during initial development and over time. This paper will discuss experiences using SEDRIS in this context. It will examine the basic representational requirements that needed to be met and the interchange problems that were to be overcome. The ways in which SEDRIS was seen to address these problems will be considered, along with the other advantages SEDRIS offered. Experiences developing SEDRIS software and interchanging databases using SEDRIS will also be described, including some lessons learned concerning both the use of SEDRIS and database interchange in general.


2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.


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