Matches in ScholarlyData for { ?s ?p ?o. }
- 151 hasAuthorList authorList.
- 151 isPartOf proceedings.
- 151 keyword "Poster Session".
- 151 title "A General Framework for Adaptive and Online Detection of Web Attacks".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasItem item-5.
- authorList hasLastItem item-5.
- item-1 type ListItem.
- item-1 hasContent wei-wang.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent florent-masseglia.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent thomas-guyet.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent rene-quiniou.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent marie-odile-cordier.
- item-5 previous item-4.
- 152 type InProceedings.
- 152 label "PAKE-based Mutual HTTP Authentication for Preventing Phishing Attacks".
- 152 sameAs 152.
- 152 abstract "We developed a new Web authentication protocol with passwordbased mutual authentication which prevents various kinds of phishing attacks. This protocol provides a protection of user's passwords against any phishers even if a dictionary attack is employed, and prevents phishers from imitating a false sense of successful authentication to users. The protocol is designed considering interoperability with many recent Web applications which requires many features which current HTTP authentication does not provide. The protocol is proposed as an Internet Draft submitted to IETF, and implemented in both server side (as an Apache extension) and client side (as a Mozilla-based browser and an IE-based one). Categories and Subject Descriptors: K.6.5 [Management of Computing and Information Systems]: Security and Protection— Authentication General Terms: Security, Standardization. Keywords: Network protocol, Mutual authentication, HTTP.".
- 152 hasAuthorList authorList.
- 152 isPartOf proceedings.
- 152 keyword "Poster Session".
- 152 title "PAKE-based Mutual HTTP Authentication for Preventing Phishing Attacks".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent yutaka-oiwa.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent hiromitsu-takagi.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent hajime-watanabe.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent hirofumi-suzuki.
- item-4 previous item-3.
- 153 type InProceedings.
- 153 label "Inferring Private Information Using Social Network Data".
- 153 sameAs 153.
- 153 abstract "On-line social networks, such as Facebook, are increasingly utilized by many users. These networks allow people to publish details about themselves and connect to their friends. Some of the information revealed inside these networks is private and it is possible that corporations could use learning algorithms on the released data to predict undisclosed private information. In this paper, we explore how to launch inference attacks using released social networking data to predict undisclosed private information about individuals. We then explore the effectiveness of possible sanitization techniques that can be used to combat such inference attacks under different scenarios. social network data could be used to predict some individual private trait that a user is not willing to disclose (e.g., political or religious affiliation) and explore the effect of possible data sanitization alternatives on preventing such private information leakage. To our knowledge this is the first comprehensive paper that discusses the problem of inferring private traits using real-life social network data and possible sanitization approaches to prevent such inference. First, we present a ıve modification of Na¨ Bayes classification that is suitable for classifying large amount of social network data. Our modified Na¨ Bayes algorithm predicts privacy sensitive trait ıve information using both node traits and link structure. We compare the accuracy of our learning method based on link structure against the accuracy of our learning method based on node traits. Please see extended version of this paper [3] for further details of our modified Naive Bayes classifier. In order to protect privacy, we sanitize both trait (e.g., deleting some information from a user's on-line profile) and link details (e.g., deleting links between friends) and explore the effect they have on combating possible inference attacks. Our initial results indicate that just sanitizing trait information or link information may not be enough to prevent inference attacks and comprehensive sanitization techniques that involve both aspects are needed in practice. Similar to our paper, in [2], authors consider ways to infer private information via friendship links by creating a Bayesian Network from the links inside a social network. A similar privacy problem for online social networks is discussed in [4]. Compared to [2] and [4], we provide techniques that help in choosing the most effective traits or links that need to be removed for protecting privacy.".
- 153 hasAuthorList authorList.
- 153 isPartOf proceedings.
- 153 keyword "Poster Session".
- 153 title "Inferring Private Information Using Social Network Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent jack-lindamood.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent raymond-heatherly.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent murat-kantarcioglu.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent bhavani-thuraisingham.
- item-4 previous item-3.
- 154 type InProceedings.
- 154 label "Privacy Preserving Frequency Capping in Internet Banner Advertising".
- 154 sameAs 154.
- 154 abstract "We describe an optimize-and-dispatch approach for delivering pay-per-impression advertisements in online advertising. The platform provider for an advertising network commits to showing advertisers' banner ads while capping the number of advertising message shown to a unique user as the user transitions through the network. The traditional approach for enforcing frequency caps has been to use crosssite cookies to track users. However,cross-site cookies and other tracking mechanisms can infringe on the user privacy. In this paper, we propose a novel linear programming approach that decides when to show an ad to the user based solely on the page currently viewed by the users. We show that the frequency caps are fulfilled in expectation. We show the efficacy of that approach using simulation results. Categories and Subject Descriptors: G.3 Mathematics of Computing: Probability and Statistics General Terms: Algorithms. Keywords: User Model, Markov Chain to transition from one section of the advertising network to another based on a random yet know probability transition matrix. The traditional approach to frequency capping is to use cross-site cookies to track users through the web properties where the advertising network is serving ads. The cookies are used to keep a count of the number of ads the user has seen. When the user has reached the maximum daily caps, no further ads are shown. However, there has been growing concern over the privacy issues associated with tracking the user across multiple sites. Furthermore,up to 33 % of the users delete their cookies making cookie based approach unreliable [3]. We propose a novel algorithm that can be used to insure that the frequency caps are fulfilled in expectation. The approach is based on formulating a linear optimization program that maximizes the expected number of ads seen by the user subject to the frequency caps constraints. The solution to the linear program gives a set of probabilistic weights used by the ad server to decide whether to serve the ad when a user arrives at a specific web page.".
- 154 hasAuthorList authorList.
- 154 isPartOf proceedings.
- 154 keyword "Poster Session".
- 154 title "Privacy Preserving Frequency Capping in Internet Banner Advertising".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasLastItem item-1.