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In this paper, we present a privacy-preserving service selection framework for cloud-based service systems. In the cloud-based service system, a cloud provider selects the best service from a set of services based on their Quality-of-Service (QoS) information. The QoS information of services is sensitive from the service provider’s point of view. We claim that the service selection process in the cloud can be biased. A service provider can bribe a dishonest employee of the cloud provider for taking unfair advantage during a service selection process. Therefore, it is important to execute the service selection tasks keeping QoS information private. We use a fully homomorphic encryption ( F H E ) scheme in this paper for encrypting QoS values. Service selection task is performed by the cloud provider on encrypted QoS values to ensure privacy. In order to reduce computation overhead, we propose a MapReduce model for parallel execution. We conduct several experiments to evaluate the performance of our proposed privacy preserving service selection framework using synthetic QoS dataset.

We present a lossless Deoxyribonucleic Acid (DNA) sequence hiding method that can be used for ensuring authenticity of DNA sequence in the context of Mobile Cloud based healthcare systems. Hiding data within DNA sequence results in permanent information loss in DNA sequence. Therefore, providing DNA sequence authenticity using data hiding is challenging. Moreover, existing works on DNA data hiding require a reference DNA sequence data to retrieve hidden data. Hence, current methods are not blind approaches and inappropriate for ensuring authenticity of DNA sequence in the Mobile Cloud. The proposed method hides authentication data within DNA sequence, extracts authentication data, and reconstructs the DNA sequence without any loss of information. From there, our proposed approach guarantees DNA sequence authenticity and integrity in Mobile Cloud based healthcare systems. We present a security analysis of our method to show that the method is secured. We conduct several experiments to demonstrate the performance of our proposed method.

Conductometric and cloud point (CP) measurement techniques have been employed in order to investigate the interaction of antibiotic drug levofloxacin hemihydrate (LFH) with anionic surfactant, sodium dodecyl sulfate (SDS) as well as a non-ionic surfactant Tween-80 (Tw-80; polyethylene sorbitol ester) in the absence/occurrence of various salts over the temperature range 298.15–318.15 K keeping regular interval of 5 K. Two clear c* values were extracted for pure SDS along with (SDS + LFH) mixtures where the increase and decrease of c* values due to the addition of LFH and salts respectively demonstrated the happened interaction among the studied components where salts provided a convenient environment for aggregation of surfactants. Negative ∆G°m values of SDS in H2O/(SDS + LFH) mixtures revealed the spontaneous assembling of surfactant molecules and the stability of the aggregates. The values of ∆H°m and ∆S°m indicate the existence of hydrophobic & electrostatic interactions between drug (LFH) and surfactant (SDS). Enthalpy and entropy contributions on the standard free energy of micellization were also calculated. The CP magnitudes of Tween-80 were reduced with increasing its concentration. The occurrence of LFH/(LFH + salts) boosted to lessening the CP values. The positive ∆G°c values suggested the non-spontaneity of clouding while the obtained ∆H°c and ∆S°c values of this CP study demonstrated the two significant accelerating impacts (electrostatic/hydrophobic interactions) between LFH & Tw-80. The thermodynamic properties of transfer for both techniques were also evaluated and analyzed in detail in the current study.

We present a privacy-preserving framework for Artificial Intelligence (AI) enabled composition for the edge networks. Edge computing is a very promising technology for provisioning realtime AI services due to low response time and network bandwidth requirements. Due to the lack of computational capabilities, an edge device alone cannot provide the complex AI services. Complex AI tasks should be divided into multiple sub-tasks and distributed among multiple edge devices for efficient service provisioning in the edge network. AI-enabled or automatic service composition is one of the essential AI tasks in the service provisioning. In edge computing-based service provisioning, service composition related tasks need to be offloaded to several edge nodes for efficient service. Edge nodes can be used for monitoring services, storing Quality-of-Service (QoS) data, and composing services to find the best composite service. Existing service composition methods use plaintext QoS data. Hence, attackers may compromise edge devices to reveal QoS data of services and modify them for giving an advantage to particular edge service providers, and the AI-based service composition becomes biased. From that point of view, a privacy-preserving framework for AI-based service composition is required for the edge networks. In our proposed framework, we introduce an AI-based composition model for edge services in the edge networks. Additionally, we present a privacy-preserving AI service composition framework to perform composition on encrypted QoS data using fully homomorphic encryption (FHE) algorithm. We conduct several experiments to evaluate the performance of our proposed privacy-preserving service composition framework using a synthetic QoS dataset.