A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications

Chessa, Michela; Großklags, Jens; Loiseau, Patrick

doi:10.1109/csf.2015.14

Cited by 32 publications

(26 citation statements)

References 53 publications

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“…The first set of papers study privacy that is obtained by perturbing sensitive data. Chessa et al [2015] model the interaction between a set of users who contribute data in order to identify mechanisms by which the learning agent can improve the quality of its estimation. The users have two strategic variables: whether to contribute at all, and the level of precision to use if they do contribute.…”

Section: Perturbationmentioning

confidence: 99%

“…They find that an analyst can shift the equilibrium of the game towards more accurate estimation by imposing a minimum level of precision in the strategy space of the users. The paper studies computation of the average of a data set as a foundation for more complex statistics [Chessa et al 2015]. Shokri [2015] also addresses the issue of data privacy for users who send private information to an untrusted source.…”

Section: Perturbationmentioning

confidence: 99%

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A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

2019

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Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this paper, we survey 24 articles from 2008-2018 that use game theory to model defensive deception for cybersecurity and privacy. Then we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models which can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.

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Section: Perturbationmentioning

confidence: 99%

Section: Perturbationmentioning

confidence: 99%

A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

2019

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“…In our problem, an individual needs to decide whether to participate in the data collection. In the studies in [21], [22], [27], [28], however, an individual decides on the variance of noise to be added to his reported data (as he will always participate). Moreover, regarding the individuals' decision modeling, none of the previous studies (e.g., [15]- [28]) explored the behavioral economics modeling of individuals.…”

Section: Literature Reviewmentioning

confidence: 99%

“…p pos ) − g( n pos ) in(21). Then the approximated optimal solution˜ * pos under the case of a positive reference point is the root of f pos ( ) = 0 in the feasible set { : g( n pos ) − g(…”

mentioning

confidence: 99%

Prospect Theoretic Analysis of Privacy-Preserving Mechanism

Liao

Chen

Huang

2020

IEEE/ACM Trans. Networking

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We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat to the contributors, the data collector adopts a privacy-preserving mechanism by adding random noise to the computation result, at the cost of reduced accuracy. Individuals decide whether to contribute data when faced with the privacy issue. Due to the intrinsic uncertainty in privacy protection, we model individuals' privacy-related decision using Prospect Theory. Such a theory more accurately models individuals' behavior under uncertainty than the traditional expected utility theory, whose prediction always deviates from practical human behavior. We show that the data collector's utility maximization problem involves a polynomial of high and fractional order, the root of which is difficult to compute analytically. We get around this issue by considering a large population approximation, and obtain a closed-form solution that well approximates the precise solution. We discover that the data collector who considers the more realistic Prospect Theory based individual decision modeling would adopt a more conservative privacy-preserving mechanism, compared with the case based on the expected utility theory modeling. We also study the impact of Prospect Theory parameters, and concludes that more loss-averse or risk-seeking individuals will trigger a more conservative mechanism. When individuals have different Prospect Theory parameters, simulations demonstrate that the privacy protection first becomes stronger and then becomes weaker as the heterogeneity increases from a low value to a high one.

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“…Another indicator of how habits, mores, and cultural constraints influence payoffs is to examine cases in which producers can choose their level of contribution. An existing game-theoretic model [10] suggests that the more trustworthy and precise the data generated by producers, the more likely an equilibrium will develop between producers and consumers. When participants in a user-generated content (UGC) community can choose their degree of contribution, they tend to be highly motivated in their subsequent participation [11].…”

Section: Prior Work and Backgroundmentioning

confidence: 99%

Data Reuse and the Social Capital of Open Science

Alicea

2016

Preprint

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Participation in Open Data initiatives require two semi-independent actions: the sharing of data produced by a researcher or group, and a consumer of shared data. Consumers of shared data range from people interested in validating the results of a given study to transformers of the data. These transformers can add value to the dataset by extracting new relationships and information. The relationship between producers and consumers can be modeled in a game-theoretic context, namely by using a Prisoners' Dilemma (PD) model to better understand potential barriers and benefits of sharing. In this paper, we will introduce the problem of data sharing, consider assumptions about economic versus social payoffs, and provide simplistic payoff matrices of data sharing. Several variations on the payoff matrix are given for different institutional scenarios, ranging from the ubiquitous acceptance of Open Science principles to a context where the standard is entirely non-cooperative. Implications for building a CC-BY economy are then discussed in context.

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A Game-Theoretic Study on Non-monetary Incentives in Data Analytics Projects with Privacy Implications

Cited by 32 publications

References 53 publications

A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

Prospect Theoretic Analysis of Privacy-Preserving Mechanism

Data Reuse and the Social Capital of Open Science

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