Privacy-Preserving Schemes for Ad Hoc Social Networks: A Survey

Sentiment analysis (SA) is a technique aimed at extracting opinions and sentiments through the analysis of text, often used in healthcare research to understand patients’ needs and interests. Data from social networks, such as Twitter, can provide useful insights on sexual behavior. We aimed to assess the perception of Valentine’s Day by performing SA on tweets we collected between 28 January and 13 February 2019. Analysis was done using ad hoc software. A total of 883,615 unique tweets containing the word “valentine” in their text were collected. Geo-localization was available for 48,918 tweets; most the tweets came from the US (36,889, 75.41%), the UK (2605, 5.33%) and Canada (1661, 3.4%). The number of tweets increased approaching February 14. “Love” was the most recurring word, appearing in 111,981 tweets, followed by “gift” (55,136), “special” (34,518) and “happy” (33,913). Overall, 7318 tweets mentioned “sex”: among these tweets, the most recurring words were “sexy” (2317 tweets), “love” (1394) and “gift” (679); words pertaining to intimacy and sexual activity, such as “lingerie”, “porn”, and “date” were less common. In conclusion, tweets about Valentine’s Day mostly focus on the emotions, or on the material aspect of the celebration, and the sexual aspect of Valentine’s Day is rarely mentioned.

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RAPP: An efficient revocation scheme with authentication and privacy preserving for vehicular ad-hoc networks

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) ◽

10.1109/iccke.2017.8167905 ◽

2017 ◽

Cited By ~ 1

Author(s):

Amirreza Sarencheh ◽

Maryam Rajabzadeh Asaar ◽

Mahmoud Salmasizadeh ◽

Mohammad Reza Aref

Keyword(s):

Ad Hoc Networks ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Privacy Preserving ◽

Hoc Networks

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Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

Future Internet ◽

10.3390/fi13020023 ◽

2021 ◽

Vol 13 (2) ◽

pp. 23

Author(s):

Angeliki Kitsiou ◽

Eleni Tzortzaki ◽

Christos Kalloniatis ◽

Stefanos Gritzalis

Keyword(s):

Social Networks ◽

Interdisciplinary Approach ◽

Privacy Preserving ◽

Social Characteristics ◽

Behavioral Models ◽

Social Landscape ◽

Technological Context ◽

Privacy Risks ◽

Self Adaptive

Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving schemes have been developed, considered the importance of users’ social and technological context and specific privacy criteria that should be satisfied. However, under the current self-adaptive privacy approaches, the examination of users’ social landscape interrelated with their privacy perceptions and practices, is not thoroughly considered, especially as far as users’ social attributes concern. This study, aimed at elaborating this examination in depth, in order as to identify the users’ social characteristics and privacy perceptions that can affect self-adaptive privacy design, as well as to indicate self-adaptive privacy related requirements that should be satisfied for users’ protection in SNs. The study was based on an interdisciplinary research instrument, adopting constructs and metrics from both sociological and privacy literature. The results of the survey lead to a pilot taxonomic analysis for self-adaptive privacy within SNs and to the proposal of specific privacy related requirements that should be considered for this domain. For further establishing of our interdisciplinary approach, a case study scenario was formulated, which underlines the importance of the identified self-adaptive privacy related requirements. In this regard, the study provides further insight for the development of the behavioral models that will enhance the optimal design of self-adaptive privacy preserving schemes in SNs, as well as designers to support the principle of PbD from a technical perspective.

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