Biblio

In the verifiable database (VDB) model, a computationally weak client (database owner) delegates his database management to a database service provider on the cloud, which is considered untrusted third party, while users can query the data and verify the integrity of query results. Since the process can be computationally costly and has a limited support for sophisticated query types such as aggregated queries, we propose in this paper a framework that helps bridge the gap between security and practicality trade-offs. The proposed framework remodels the verifiable database problem using Stackelberg security game. In the new model, the database owner creates and uploads to the database service provider the database and its authentication structure (AS). Next, the game is played between the defender (verifier), who is a trusted party to the database owner and runs scheduled randomized verifications using Stackelberg mixed strategy, and the database service provider. The idea is to randomize the verification schedule in an optimized way that grants the optimal payoff for the verifier while making it extremely hard for the database service provider or any attacker to figure out which part of the database is being verified next. We have implemented and compared the proposed model performance with a uniform randomization model. Simulation results show that the proposed model outperforms the uniform randomization model. Furthermore, we have evaluated the efficiency of the proposed model against different cost metrics.

Trust and reputation techniques have offered favorable solutions to the web service selection problem. In distributed systems, service consumers identify pools of service providers that offer similar functionalities. Therefore, the selection task is mostly influenced by the non-functional requirements of the consumers captured by a varied number of QoS metrics. In this paper, we present a QoS-aware trust model that leverages the correlation information among various QoS metrics. We compute the trustworthiness of web services based on probability theory by exploiting two statistical distributions, namely, Dirichlet and generalized Dirichlet, which represent the distributions of the outcomes of multi-dimensional correlated QoS metrics. We employ the Dirichlet and generalized Dirichlet when the QoS metrics are positively or negatively correlated, respectively. Experimental results endorse the advantageous capability of our model in capturing the correlation among QoS metrics and estimating the trustworthiness and reputation of service providers.