# Next-Generation Blockchain Technology:
## 7 Entropic Differential Barcode (EDB)  
For any given digital asset, one can use the entropic barcode technology to generate an entropic barcode of the original asset, not only for the purpose of labeling, compressing, and encrypting but also for detecting any changes in it at later time. Detecting changes in the digital assets is a very powerful application because it allows one to check the integrity of a digital asset without accessing any information within it, maintaining full data privacy. Essentially, after the entropic barcode is generated, any changes to the original digital asset, as small as just one bit, will be easily detected regardless of its size. This method involves reconstructing the entropic barcode of the digital asset again and then comparing it to the original entropic barcode. The comparison between the two barcodes is performed via the entropic differential barcode (EDB), which is obtained by subtracting the two barcodes from each other. In a previous study using a similar method and applying it to genetic sequences, the entropic ratio was used instead of the entropic differential barcode to detect genetic mutations [10]. However, it is very possible that sections of the dataset might have IE = 0. This creates a problem when applying the ratio of the two spectra/barcodes as a number divided by zero is not computable. Hence, to avoid this problem, a better method based on the entropic differential barcode (EDB) is introduced. If the asset is unchanged, the EDB will be 0 everywhere. If the asset suffered any changes, the EDB will show deviations from 0 at the location where the changes occurred. This method could therefore be used to check the integrity of any digital asset and to detect any possible changes to it, however small. The applications are multifold and include validation of digital data files, blockchains, and crypto tokens. To demonstrate the method of entropic barcoding and entropic differential barcoding with respect to digital files, we will here provide an example. Figure 6a shows an example of an Excel spreadsheet digital file for which we produced its unique entropic barcode representation (Figure 6b) of the original file. In this example, the Excel file has \(N=655\),360 bits. For this particular example, we imposed 1000 points of barcode size, i.e., the number of windows created equals 1000, \(N_{W}=1000\), obtaining WS = 655. The SS has been taken equal to the WS; WS = SS = 655. The above example has been generated using an \(m=3\) characters \(m\)-block with ss = \(m=3\). The maximum possible IE per window is therefore 3. The entire barcode contains data with maximum IE = 2.9765, minimum IE = 0, and average \(\langle\text{IE}\rangle=1.5418\). For a 1000-points entropic barcode, the resulting spectrum text file is always 15 kilobytes (Kb), regardless of the type or size of the original file. Hence, this technique can achieve significant data compression from multiple gigabytes (Gb) to 15 Kb, while the entropic barcode is a true and unique representation of the original file, which does not disclose any of the file's content. After the barcode is generated, any changes or alterations to the original file, as small as just one bit, will be easily picked up. This method could be used to check for digital fraud and to determine the integrity of any digital file, including financial data files, banking records, image files, any type of document or software, blockchains, non-fungible tokens (NFTs), etc. Moreover, this method allows one to preserve the digital content of the file into this format while maintaining the full confidentiality of the file's content, i.e., the entropic barcode does not reveal any information contained within the file. For example, one can have a digital copy of the entropic barcode of the spreadsheet Excel file analyzed here, and there would be no way of extracting any numerical or financial data contained in the file from the barcode itself.