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Enhanced models for privacy and utility in continuous-time diffusion networks

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Abstract

Controlling the propagation of information in social networks is a problem of growing importance. On one hand, users wish to freely communicate and interact with their peers. On the other hand, the information they spread can bring to harmful consequences if it falls in the wrong hands. There is therefore a trade-off between utility, i.e. reaching as many intended nodes as possible, and privacy, i.e. avoiding the unintended ones. The problem has attracted the interest of the research community: some models have already been proposed to study how information propagates and to devise policies satisfying the intended privacy and utility requirements. In this paper, we adapt the basic framework of Backes et al. to include more realistic features, that in practice influence the way in which information is passed around. More specifically, we consider: (a) the topic of the shared information, (b) the time spent by users to forward information among them and (c) the user social behaviour. For all features, we show a way to reduce our model to the basic one, thus allowing the methods provided in the original paper to cope with our enhanced scenarios. Furthermore, we propose an enhanced formulation of the utility/privacy policies, to maximize the expected number of reached users among the intended ones, while minimizing this number among the unintended ones, and we show how to adapt the basic techniques to these enhanced policies. We conclude by giving a new approach to the maximization/minimization problem by finding a trade-off between the risk and the gain function through biobjective optimization.

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Notes

  1. Even though, this notion of privacy might be known as confidentiality or secrecy, for the sake of continuity we adopt the terminology as done in [1].

  2. This methodology can be seen as the gradient descent method for minimizing continuous differentiable functions: we start from a random point y and we iteratively move in the direction of the steepest descent, as defined by the negative of the gradient.

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Acknowledgements

This study was funded by the ERC project Hypatia under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No: 835294). We thank the anonymous reviewers for their positive attitude towards our paper and for several suggestions that improved our presentation.

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Authors and Affiliations

  1. Inria Saclay - École Polytechnique - IPP, Palaiseau, France

    Federica Granese & Catuscia Palamidessi

  2. Department of Computer Science - Sapienza, “Sapienza” University of Rome, Rome, Italy

    Federica Granese & Daniele Gorla

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  1. Federica Granese
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  2. Daniele Gorla
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  3. Catuscia Palamidessi
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Correspondence to Federica Granese.

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Granese, F., Gorla, D. & Palamidessi, C. Enhanced models for privacy and utility in continuous-time diffusion networks. Int. J. Inf. Secur. 20, 763–782 (2021). https://doi.org/10.1007/s10207-020-00530-7

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