## 6 Evaluation  
Secret sharing schemes have been used successfully in data splitting and reconstruction, thereby providing data security in a keyless manner. This section outlines an experiment involving three of the main contenders for Secret Sharing schemes in Cloud-based systems: [PERSON]'s Perfect Secret Sharing Scheme (PSS), [PERSON]'s Secret Sharing made short or Computational Secret Sharing scheme (CSS) and [PERSON]'s Information Dispersal Algorithm (IDA), see Tables 1-8. The experiments were conducted in collaboration with a commercial partner, Payfont Limited, that has further validated the results below in a highly scaled, commercial technical infrastructure. The evaluator in this case is the performance overhead at increasing thresholds and data sizes show varied behaviours that depict their strengths and weaknesses at different application scenarios. Figures 3-6 outline the results. Figure 4: Share creation and recreation against increasing data sizes (\(n=10\); \(t=4\)). Figure 5: Graphs of Time taken against Data sizes in share creation (**a**) and recreation (**b**) when \(n=10\), \(t=6\). Figure 3: Share creation and recreation against increasing data sizes (\(n=5\); \(t=\)2). Two different experiments were performed on two different machines. The aim of the experiments were to demonstrate the implication data size variations have on the performance of each secret sharing scheme (SSS) algorithm in terms of share creation and share recreation. Data sizes from 1024 KB to 16,384 KB and 2 KB to 262, 144 KB were evaluated on machines (a) and (b) respectively. The data generated are arbitrary due to the fact that the evaluations are not catered for in relation to one specific area where SSS algorithms may be applied in. The test machines are (a) a D-Series 3 specification Microsoft Azure virtual machine which consists of 4 vCores, 14 GB of RAM and a 200 GB SSD; and (b) an HP EliteDesk 800 G1 USDT, X64-based PC running on Intel(r) Core(tm)i5-4570S CPU _@_ 2.90 GHz, 2901 MHz, 4 cores, 4 GB RAM and a 460 GB SATA drive. Four primary sets of results were presented which use the parameters of (\(n=5\), \(t=2\)) and (\(n=10\), \(t=4\)) on machine (a) and (\(n=5\), \(t=3\)) and (\(n=10\), \(t=6\)) on machine (b). The variable \(n\) relates to the  
Figure 6: Graph of Time taken against Data sizes in share creation (**a**) and recreation (**b**) when \(n=5\), \(t=3\). number of shares to create while the variable \(t\) relates to the number of shares required for recreation of the original arbitrary data (using each SSS algorithm). Time taken for share creation and recreation unit is presented in milliseconds. From Figures 3-6 and Tables 1-8, it can be clearly demonstrated that IDA is the fastest algorithm regardless of data size. CSS comes second in terms of time taken for share creation and recreation while PSS comes last. Some significant observations in the results presented is that PSS demonstrates greater issues in regards to scalability as the data size increases in comparison with the other two algorithms and as it gets beyond 131,072 KB on machine (b) shares could no longer be created nor recreated as the machine throws up out of memory exceptions. Additionally, as we increase the parameters from \(n=5\); \(t=2\) to \(n=10\); \(t=4\) and from \(n=5\); \(t=3\); \(n=10\); \(t=6\) it can be demonstrated that only share creation will produce significant increase in performance