Trust Based Resolving of Conflicts for Collaborative Data Sharing in Online Social Networks

Shetty, Nisha P.; Muniyal, Balachandra; Prakhar, Pratyay; Singh, Angad; Batra, Gunveen; Puri, Akshita; Manjunath, Divya Bhanu; Jain, Vidit Vinay

doi:10.1007/978-981-19-4052-1_5

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…In the case of 𝑈 2 and 𝑈 3 being collaborators of the shared data and 𝑈 1 being the proprietor: PR and SL are calculated based on the negotiating users and sensitivity level-an arbitrary value-of each user. These values are calculated as follows [21], [22]: − Privacy risk (PR): The privacy risk of collaborative data is said to be the possible harm to the privacy of controllers concerning the shared data item; the higher the privacy risk, the greater the threat to controllers' privacy. The privacy risk is heightened if a greater number of proprietors trust the users in the conflicting segment 𝑛 𝛼 , the sensitivity of the data is higher and if the data item spreads widely across the network.…”

Section: Calculating Privacy Risk and Sharing Lossmentioning

confidence: 99%

Collusion-resistant multiparty data sharing in social networks

Shetty,

Muniyal,

Proothi

et al. 2024

IJECE

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The number of users on online social networks (OSNs) has grown tremendously over the past few years, with sites like Facebook amassing over a billion users. With the popularity of OSNs, the increase in privacy risk from the large volume of sensitive and private data is inevitable. While there are many features for access control for an individual user, most OSNs still need concrete mechanisms to preserve the privacy of data shared between multiple users. The proposed method uses metrics such as identity leakage (IL) and strength of interaction (SoI) to fine-tune the scenarios that use privacy risk and sharing loss to identify and resolve conflicts. In addition to conflict resolution, bot detection is also done to mitigate collusion attacks. The final decision to share the data item is then ascertained based on whether it passes the threshold condition for the above metrics.

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Section: Calculating Privacy Risk and Sharing Lossmentioning

confidence: 99%

Collusion-resistant multiparty data sharing in social networks

Shetty,

Muniyal,

Proothi

et al. 2024

IJECE

View full text Add to dashboard Cite

show abstract

Dynamic Twitter friend grouping based on similarity, interaction, and trust to account for ever‐evolving relationships

Shetty,

Muniyal,

Maben

et al. 2024

IET Communications

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Online social networks have become ubiquitous, allowing users to share opinions on various topics. However, oversharing can compromise privacy, leading to potential blackmail or fraud. Current platforms lack friend categorization based on trust levels. This study proposes simulating real‐world friendships by grouping users into three categories: acquaintances, friends, and close friends, based on trust and engagement. It also introduces a dynamic method to adjust relationship status over time, considering users' past and present offenses against peers. The proposed system automatically updates friend lists, eliminating manual grouping. It calculates relationship strength by considering all components of online social networks and trust variations caused by user attacks. This method can be integrated with clustering algorithms on popular platforms like Facebook, Twitter, and Instagram to enable constrained sharing. By implementing this system, users can better control their information sharing based on trust levels, reducing privacy risks. The dynamic nature of the relationship status adjustment ensures that the system remains relevant as user interactions evolve over time. This approach offers a more nuanced and secure social networking experience, reflecting real‐world relationship dynamics in the digital sphere.

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Trust Based Resolving of Conflicts for Collaborative Data Sharing in Online Social Networks

Cited by 2 publications

References 25 publications

Collusion-resistant multiparty data sharing in social networks

Collusion-resistant multiparty data sharing in social networks

Dynamic Twitter friend grouping based on similarity, interaction, and trust to account for ever‐evolving relationships

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