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 1 publication

(1 citation statement)

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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%