We present a solution to the problem of privacy invasion in a multiparty digital rights management scheme. (Roaming) users buy content licenses from a content provider and execute it at any nearby content distributor. Our approach, which does not need any trusted third party--in contrast to most related work on privacy-preserving DRM--is based on a re-encryption scheme that runs on any mobile Android device. Only a minor security-critical part needs to be performed on the device's smartcard which could, for instance, be a SIM card.
The anonymous payment scheme used in this paper has been introduced by @cite_9 . Analyses of this method have proven it to be more suited for our application in comparison to the frequently cited scheme by @cite_8 in @cite_17 . This is because of the chosen system being more flexible. Another reason behind choosing this protocol is that the (POS) devices used provide another layer of anonymity for the user since these device serve as a proxy between the user and the payee. Contrasting the basic version @cite_9 , however, we provide extensions such as the payment of change in case the user does not have the right amount of money in his her wallet. The anonymous payment scheme does not allow any party to get to know which content has been purchased if the user makes legitimate payments only. If the user tries to defraud some party, however, his her identity can be unveiled so that he she can be held accountable.
@cite_9 @cite_17 @cite_8
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In traditional television (TV) systems, users have to pay for all of the channels provided by the TV service server. However, usually, users are not interested in all of the channels, so they waste money by paying for all of the channels. For this reason, the concept of “pay-TV” has been developed. A pay-TV system allows the subscribers to pay for the specific channels they want to watch. If we consider that m users subscribe to n channels in traditional schemes for pay-TV, the time complexity will be O(mn). It is lower efficiency for user on the pay-TV system. But, in this paper, we proposed a novel authentication scheme for pay-TV systems based on Chebyshev chaotic maps, and the time complexity only needs O(m). The purpose of our scheme is focus on efficiency and security. The security and performance analyses showed that our proposed scheme satisfies the essential functionality requirements, withstands potential attacks, and is suitable for real-world practical implementation. Furthermore, the Burrows–Abadi–Needham logic model was used to prove the correctness of our proposed scheme for the mutual authentication between users and the server. Copyright © 2016 John Wiley & Sons, Ltd. Initiating and bootstrapping secure, yet low-cost, ad-hoc transactions is an important challenge that needs to be overcome if the promise of mobile and pervasive computing is to be fulfilled. For example, mobile payment applications would benefit from the ability to pair devices securely without resorting to conventional mechanisms such as shared secrets, a Public Key Infrastructure (PKI), or trusted third parties. A number of methods have been proposed for doing this based on the use of a secondary out-of-band (OOB) channel that either authenticates information passed over the normal communication channel or otherwise establishes an authenticated shared secret which can be used for subsequent secure communication. A key element of the success of these methods is dependent on the performance and effectiveness of the OOB channel, which usually depends on people performing certain critical tasks correctly. In this paper, we present the results of a comparative usability study on methods that propose using humans to implement the OOB channel and argue that most of these proposals fail to take into account factors that may seriously harm the security and usability of a protocol. Our work builds on previous research in the usability of pairing methods and the accompanying recommendations for designing user interfaces that minimise human mistakes. Our findings show that the traditional methods of comparing and typing short strings into mobile devices are still preferable despite claims that new methods are more usable and secure, and that user interface design alone is not sufficient in mitigating human mistakes in OOB channels. There is an alarming increase in the number of cybercrime incidents through anonymous e-mails. The problem of e-mail authorship attribution is to identify the most plausible author of an anonymous e-mail from a group of potential suspects. Most previous contributions employed a traditional classification approach, such as decision tree and Support Vector Machine (SVM), to identify the author and studied the effects of different writing style features on the classification accuracy. However, little attention has been given on ensuring the quality of the evidence. In this paper, we introduce an innovative data mining method to capture the write-print of every suspect and model it as combinations of features that occurred frequently in the suspect's e-mails. This notion is called frequent pattern, which has proven to be effective in many data mining applications, but it is the first time to be applied to the problem of authorship attribution. Unlike the traditional approach, the extracted write-print by our method is unique among the suspects and, therefore, provides convincing and credible evidence for presenting it in a court of law. Experiments on real-life e-mails suggest that the proposed method can effectively identify the author and the results are supported by a strong evidence. Mobile devices with geo-positioning capabilities (e.g., GPS) enable users to access information that is relevant to their present location. Users are interested in querying about points of interest (POI) in their physical proximity, such as restaurants, cafes, ongoing events, etc. Entities specialized in various areas of interest (e.g., certain niche directions in arts, entertainment, travel) gather large amounts of geo-tagged data that appeal to subscribed users. Such data may be sensitive due to their contents. Furthermore, keeping such information up-to-date and relevant to the users is not an easy task, so the owners of such data sets will make the data accessible only to paying customers. Users send their current location as the query parameter, and wish to receive as result the nearest POIs, i.e., nearest-neighbors (NNs). But typical data owners do not have the technical means to support processing queries on a large scale, so they outsource data storage and querying to a cloud service provider. Many such cloud providers exist who offer powerful storage and computational infrastructures at low cost. However, cloud providers are not fully trusted, and typically behave in an honest-but-curious fashion. Specifically, they follow the protocol to answer queries correctly, but they also collect the locations of the POIs and the subscribers for other purposes. Leakage of POI locations can lead to privacy breaches as well as financial losses to the data owners, for whom the POI data set is an important source of revenue. Disclosure of user locations leads to privacy violations and may deter subscribers from using the service altogether. In this paper, we propose a family of techniques that allow processing of NN queries in an untrusted outsourced environment, while at the same time protecting both the POI and querying users’ positions. Our techniques rely on mutable order preserving encoding (mOPE), the only secure order-preserving encryption method known to-date. We also provide performance optimizations to decrease the computational cost inherent to processing on encrypted data, and we consider the case of incrementally updating data sets. We present an extensive performance evaluation of our techniques to illustrate their viability in practice.