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Once personal data is published online, it is out of the control of the user and can be a threat to users' privacy. Retroactively deleting data after it has been published is notoriously unreliable due to the distributed and open nature of the Internet. Cryptographic approaches implementing data revocation address this problem, but have serious limitations when considering practical deployment, and they have not been broadly adopted. In this paper, we tackle the problem of data revocation from a different perspective by examining how contractual agreements can be applied to create incentives for providers to conform to expiration regulations. Specifically, we propose a protocol to automate the handling of data revocation. We have implemented a prototype smart contract on a local Ethereum blockchain to demonstrate the feasibility of our approach. Our approach has distinct advantages over existing proposals: It can deal with a wide spectrum of revocation conditions, it can be applied retroactively after data has been published, and it does not require additional effort for users accessing the published data. It thus constitutes an interesting, novel approach to data revocation.
Once personal data is published online, it is out of the control of the user and can be a threat to users' privacy. Retroactively deleting data after it has been published is notoriously unreliable due to the distributed and open nature of the Internet. Cryptographic approaches implementing data revocation address this problem, but have serious limitations when considering practical deployment, and they have not been broadly adopted. In this paper, we tackle the problem of data revocation from a different perspective by examining how contractual agreements can be applied to create incentives for providers to conform to expiration regulations. Specifically, we propose a protocol to automate the handling of data revocation. We have implemented a prototype smart contract on a local Ethereum blockchain to demonstrate the feasibility of our approach. Our approach has distinct advantages over existing proposals: It can deal with a wide spectrum of revocation conditions, it can be applied retroactively after data has been published, and it does not require additional effort for users accessing the published data. It thus constitutes an interesting, novel approach to data revocation.
Purpose Over the past decade, many research works in various disciplines have benefited from the endless ocean of people and their potentials (in the form of crowdsourcing) as an effective problem-solving strategy and computational model. But nothing interesting is ever completely one-sided. Therefore, when it comes to leveraging people's power, as the dark side of crowdsourcing, there are some possible threats that have not been considered as should be, such as recruiting black hat crowdworkers for organizing targeted adversarial intentions. The purpose of this paper is to draw more attention to this critical issue through investigation of its different aspects. Design/methodology/approach To delve into details of such malicious intentions, the related literature and previous researches have been studied. Then, four major typologies for adversarial crowdsourced attacks as well as some real-world scenarios are discussed and delineated. Finally, possible future threats are introduced. Findings Despite many works on adversarial crowdsourcing, there are only a few specific research studies devoted to considering the issue in the context of cyber security. In this regard, the proposed typologies (and addressed scenarios) for such human-mediated attacks can shed light on the way of identifying and confronting such threats. Originality/value To the best of the authors' knowledge, this the first work in which the titular topic is investigated in detail. Due to popularity and efficiency of leveraging crowds' intelligence and efforts in a wide range of application domains, it is most likely that adversarial human-driven intentions gain more attention. In this regard, it is anticipated that the present research study can serve as a roadmap for proposing defensive mechanisms to cope with such diverse threats.
Cryptographic algorithms have been used not only to create robust ciphertexts but also to generate cryptograms that, contrary to the classic goal of cryptography, are meant to be broken. These cryptograms, generally called puzzles, require the use of a certain amount of resources to be solved, hence introducing a cost that is often regarded as a time delay—though it could involve other metrics as well, such as bandwidth. These powerful features have made puzzles the core of many security protocols, acquiring increasing importance in the IT security landscape. The concept of a puzzle has subsequently been extended to other types of schemes that do not use cryptographic functions, such as CAPTCHAs, which are used to discriminate humans from machines. Overall, puzzles have experienced a renewed interest with the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In this article, we provide a comprehensive study of the most important puzzle construction schemes available in the literature, categorizing them according to several attributes, such as resource type, verification type, and applications. We have redefined the term puzzle by collecting and integrating the scattered notions used in different works, to cover all the existing applications. Moreover, we provide an overview of the possible applications, identifying key requirements and different design approaches. Finally, we highlight the features and limitations of each approach, providing a useful guide for the future development of new puzzle schemes.
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