Prying Data out of a Social Network

Bonneau, Joseph; Anderson, Jonathan; Danezis, George

doi:10.1109/asonam.2009.45

Cited by 306 publications

(75 citation statements)

References 10 publications

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“…On the back of this come debates about the ways in which use may actual constitute misuse. The literature here is simply huge, encompassing both quite discrete topics such as the notion of attack surfaces [ [9][76], and much broader ones such as privacy [6] [51]. The latter may also be seen to encompass matters such as the use of personal data for the purposes of shame and ridicule, exposure, retribution, bullying, and even blackmail [32][46] [69].…”

Section: The Use Of Personal Data and Associated Threatsmentioning

confidence: 99%

The practical politics of sharing personal data

Tolmie

Crabtree

2017

Pers Ubiquit Comput

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The focus of this paper is upon how people handle the sharing of personal data as an interactional concern. A number of ethnographic studies of domestic environments are drawn upon in order to articulate a range of circumstances under which data may be shared. In particular a distinction is made between the in situ sharing of data with others around you and the sharing of data with remote parties online. A distinction is also drawn between circumstances of purposefully sharing data in some way and circumstances where the sharing of data is incidental or even unwitting. On the basis of these studies a number of the organisational features of how people seek to manage the ways in which their data is shared are teased out. The paper then reflects upon how data sharing practices have evolved to handle the increasing presence of digital systems in people's environments and how these relate to the ways in which people traditionally orient to the sharing of information. In conclusion a number of ways are pointed out in which the sharing of data remains problematic and there is a discussion of how systems may need to adapt to better support people's data sharing practices in the future.

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Section: The Use Of Personal Data and Associated Threatsmentioning

confidence: 99%

The practical politics of sharing personal data

Tolmie

Crabtree

2017

Pers Ubiquit Comput

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“…Similarly, Danezis [2] proposed a machine-learning based approach to automatically extract privacy settings from the social context within which the data is produced. Parallel to the work of Danezis, AduOppong et al [3] develop privacy settings based on a concept of "Social Circles" which consist of clusters of friends formed by partitioning users' friend lists.…”

Section: Existing Methodologymentioning

confidence: 99%

A Survey on A3P System for Image Uploading

2015

IJSR

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With the sharing of images on social media such as Facebook, twitter, etc. increases, maintain their privacy becomes the major problem. As user shares their private images on social sites, people expect more tools to allow them to regain control over their privacy. By considering this need, we propose an Adaptive Privacy Policy Prediction (A3P) system which provides user convenient privacy settings by automatically generating personalized policies. To define users' privacy preferences we consider the different factors such as social environment, personal characteristics, image content and metadata. For the images being uploaded, we define the best available privacy policy for the user based on the users' available history on the site. For that we propose a two level framework. A3P system relies on the image classification framework for image categories which may be associated with similar policies and on a policy prediction algorithm to automatically generate a policy for each newly uploaded image, also according to users' social features.

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“…The validity or originality behind the acquired and shared content makes a strong trust factor among users in the group which later gets multiplied within other users also. [5] …”

Section: Social Networking Based Learning Patternsmentioning

confidence: 99%

Formalization of Learning Patterns Through SNKA

Rajesh¹,

Keator²

2016

ijacsa

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Abstract-The Learning patterns found among the learners community is steadily progressing towards the digitalized world. The learning patterns arise from acquiring and sharing knowledge. More impact is found on the usage of knowledge sharing tools such as facebook, linkedin, weblogs, etc that are dominating the traditional means of learning. Since the knowledge patterns acquired through web unstructured data is insecure, it leads to poor decision making or decision making without a root cause. These acquired patterns are also shared to others which indirectly affect the trust patterns between users. In this paper, In order to streamline the knowledge acquisition patterns and their sharing means a new framework is defined as Social Networking based Knowledge Acquisition (SNKA) to formalize the observed data and the Dynamic Itemset Count (DIC) algorithm is tried for predicting the users about the usage of web content before and after the knowledge is acquired. Finally the rough idea in building a tool is also suggested.

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Prying Data out of a Social Network

Cited by 306 publications

References 10 publications

The practical politics of sharing personal data

The practical politics of sharing personal data

A Survey on A3P System for Image Uploading

Formalization of Learning Patterns Through SNKA

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