Edge storage presents a viable data storage alternative for application vendors (AVs), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard data integrity and counter the traditional reliance on centralized third-party auditors (TPA), which are unfit for distributed systems. Our novel approach employs edge servers (ES) as mutual auditors, eliminating the need for a centralized entity. This decentralization minimizes potential collusion with malicious auditors and biases in audit outcomes. Using a strategic game model, we demonstrate that ESs are more motivated to audit each other than TPAs. The auditing process is addressed as a Nash Equilibrium problem, assuring accurate integrity proof through incentives for ESs. Our scheme's security and performance are rigorously assessed, showing it is secure within the random oracle model, offers improved speed, and is cost-effective compared to existing methods. It also significantly reduces computational and communication overhead.

Edge storage is becoming an increasingly appealing alternative for data owners (DOs), offering benefits like decreased latency and minimized bandwidth usage compared to traditional cloud storage solutions. Nonetheless, stored data within edge servers (ESs) remains vulnerable to disruptions. Existing data integrity auditing schemes face challenges such as the costs of third-party auditors (TPA), unreliable and delayed audit results, and effective management of data inspection concerning time and energy consumption. To tackle these challenges, we introduce DIVO, a decentralized data inspection approach. DIVO leverages ESs as each others’ auditors, removing the necessity for a centralized party, thereby mitigating collision risks and potential biases in audit results. We propose a game-theoretic technique to efficiently manage data inspection and verification offloading to ESs. By formulating the decision-making issue as a strategic game for optimally allocating verification tasks among multiple ESs, we establish the presence of Nash equilibrium and design a strategy to attain it. Through comprehensive security and performance evaluations, DIVO has been shown to operate securely within the random oracle model while delivering notable efficiency improvements over recent methods. Our analysis highlights that DIVO surpasses a wide range of recent approaches in both communication and computation efficiency.

With the widespread growth of cloud computing technology, cloud software services are ubiquitous these days. Using this technology, software providers can sell their products through cloud computing environments in the pay-as-you-use fashion. However, performing secure and accurate calculations in cloud computing environments has become extremely challenging. As the data to be processed by cloud software might be highly sensitive, its confidentiality needs to be taken care of before transferring the data to the cloud server. Also, in addition to the data confidentiality, the security of algorithms employed in the software is of vital importance, and thus software owners may be worried about revealing their algorithms through the cloud server. Homomorphic cryptosystems can provide confidentiality for data to be processed online. However, the confidentiality of algorithms is still an open problem. To address this issue, we put forward a privacy-preserving joint data and function homomorphic encryption (JDF-HE) mechanism. Our JDF-HE can provide confidentiality for both algorithms and data, thereby being suitable for cloud software services. We prove the security of JDF-HE and analyze its performance by evaluating its actual execution overhead. Our performance and security analysis demonstrate that JDF-HE is secure and suitable for real-time applications.