# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 6 Proficiency Evaluation
### Computation Cost of the Integrity Checking Stages  
#### 6.2.1 Computation Cost of the Semi-Trust Server  
This stage contrasts the number of challenges, the number of smart device users, and the number of clouds with the semi-trust server's computation time. In the first instance, the semi-trust server's computation time was assessed depending on the volume of challenged files in the single smart device user and the single IoT-cloud system. The size of the challenge data was calculated to be equal to 1000 kilobytes. If the number of challenges reached 1000, then the size of the challenges data was equal to 1000 kilobytes. It can demonstrate that there is a linear relationship between the challenge number and the computation cost of the semi-trust server. In Figure 4, it is clear that when handling large volumes of challenged data, the computational cost is higher in [PERSON]'s solution [7] than in the proposed solution; this is because the challenge size key in the proposed system is (_Ck_ = 128 _bit_) and is independent of the challenged data size. In contrast with [PERSON] [7], which depends on the size of the data, when the challenge data increases, so does the  
Figure 3: Computation cost of the uploading stages in the semi-trust server. challenge key. Consequently, a highly efficient scheme has been improved in the proposed work. It can also further reduce the computation complexity. In the proposed system, the integrity of vast amounts of data (e.g., petabytes) is protected by applying the sampling integrity checking method; this is in contrast with the function of an IoT-cloud system. Service level agreements set the frequency and sample sizes. During the tests, this study concluded that about 0.223 s are required to audit 1000 challenges. However, the computational time required was likely to be small because the PC used in this study lacked the computational ability of the semi-trust server and the IoT-cloud system. This result confirms that in large-scale IoT-cloud systems, the proposed scheme for checking the system integrity is practical. Next, this review examined the computation cost of the semi-trust server of the multi-cloud batch integrity checking scheme based on the number of challenged clouds. Figure 5 shows that [PERSON]'s solution [7] incurred a higher computational cost for the semi-trust server than the suggested system. This is particularly true when the large-scale IoT-cloud system contains a large number of the IoT-cloud system and results because of complex algorithms. [PERSON] used the bilinear map, which is a complicated method and takes time to obtain results, unlike the proposed system, which uses lightweight operations, namely the cryptographic hash function, XOR, and concatenation, and calculates the results quickly. These were used in [PERSON]'s scheme [7] to support batch integrity checking for multi-cloud. In the proposed scheme, where one smart device user was selected for every ten clouds, the dependency was in the number of challenges. Lastly, the general integrity checking protocol used to support the multi-owner batch integrity checking in [PERSON]'s scheme [7] was compared (from the perspective of the computation cost of the semi-trust server) with this study's multi-smart device user batch integrity checking scheme. As can be observed in Figure 6, the computational costs could be significantly reduced when batch integrity checking for multiple owners was undertaken. Therefore, this study's solution was considerably more efficient and cost-effective than [PERSON]'s scheme [7]. This is due to using the lightweight cryptographic hash function, XOR, and concatenation operations that do not require significant time or resources to obtain results. In addition, the scheme proposed in Section 4 is short and does not contain complex operations. Conversely, [PERSON] used the Bilinear Map method, a complex algorithm that takes a long time to obtain results in a large-scale IoT-cloud system where there may be millions or billions of smart device data users.