However, technically, it is just a piece of cryptographic code of no intrinsic value. Although Blockchain and its related cryptocurrencies are a recent development, tokens have been the subject of psychological research for a long time.
In psychology, tokens are used to shape a target behavior, and therefore tokens have to be backed by reinforcers. A relation between a token of no intrinsic value with an unconditioned reinforcer like food or a conditioned one like money has to be established by reinforcement.
The law of effect Thorndike describes that behaviour which is followed by pleasant consequences as having a higher likelihood of being repeated, whereas unpleasant consequences decrease the likelihood of repeat behaviour. In humans, it is often applied in cognitive behavioral therapy and childrearing.
But how does that apply to health data and how could the tokenisation of health data be the solution of some of the most pressing problems in modern healthcare? Modern healthcare runs on data. Tremendous amounts of data are being generated by medical documentation, regulatory requirements, and patient care Raghupathi Another driving force in this massive growth of data is the individual himself when using a fitness or wellness app.
There are only estimates what personal health data are worth, but it can be deducted from what companies are willing to invest in order to gather those data. It is a low price if you take into account what pharmaceutical companies invest in order to gather clinical trial data and real-world evidence.
It results in a lack of incentives for individuals to demand or record, digitise, and update their health data. Hence, it perpetuates the current system of monopolisation of health data leading to siloes and inefficiencies instead of motivating the individual being in control of his data.
Most people are only interested in their health data when they are sick or when somebody they know is suffering from a health problem, but there are ways to motivate them to tend to their health data. It shows that healthcare systems must incentivise individuals to take control over their data. Blockchain technology can facilitate such an infrastructure in the form of a decentralised marketplace where the access to health data is under the control of the individual.
Information seekers can post their query and individuals can remain anonymous and decide whether or not they want to share their data. With the tokens in a Blockchain-based marketplace, a reward can be automatically transferred on the basis of a digital contract once the data has been delivered.
Such a system has a clear advantage over a fiat currencybased system where always a middleman has to be involved and the large population of unbanked individuals cannot participate. In Figure 1 the token economy of the Health Information Traceability platform is presented.
A payment and utility token facilitates transactions of health information. The central asset to be exchanged in the HIT-ecosystem is information that has a token value attached to it. The token value depends on how much the network participant values the information in question.
Population survey and representative surveys can be conducted via contacting individuals directly. Such a distributed ecosystem can be implemented with Blockchain technology, making transaction processes transparent and more efficient at the same time. A Blockchain-based token system is predestined to align incentives among ecosystem participants, for example, providers of health information and those who want to analyse health data.
It allows the latter to have direct access to providers of health information without the need for intermediaries. At the same time, it puts individuals in control of the use and monetisation of their health data. Tokenisation of health data motivates individuals to make their data shareable, thus solving the fundamental problem of modern healthcare. BKK Dachverband Gesundheitsreport Guo and Zhuang [ 31 ] proposed a watermarking scheme with tamper localization capability based on difference expansion.
The scheme introduces the concept of region of authentication ROA which can be flexibly partitioned into small regions as an image block or polygonal region in a multilevel hierarchical manner. A hashing function is used to produce digital signatures for each image block, which are then added to the watermark payload.
To verify authenticity of the image, the signatures for the ROA are compared to detect any tampering. Tamper localization is implemented using the concept of ROI shading. Tan et al. Each CRC is embedded into its own block. In the event that the CRC cannot be embedded into its own block, the remaining bits are carried over to the next block. Tampering is localized by extracting the watermark and comparing the CRC of each block. If both CRCs do not match, the block will be identified as being tampered, hence achieving tamper localization.
A major drawback of the proposed algorithms is their extensive usage of cryptographic CRC watermarks to implement the tamper localization functionality. Other than being computation-intensive, the algorithms provide no evidence these cryptographic watermarks were extracted intact at the receiver side. Since a 1-bit change in a CRC or hash code will lead to a false localized tamper detection, extensive use of these cryptographic primitives is considered a major limitation of the proposed algorithms.
Another drawback is the lack of evidence about robustness of the watermarks embedded in the RONI. In other words, the robustness of the algorithms was not evaluated properly using standard metrics such as normalized correlation and bit error rates to prove that the cryptographic watermarks could survive attacks such as additive noise and lossy compression. This process is described in the following sub-sections.
The proposed watermarking algorithm is based on a region-selecting property to allow for localizing tampered regions in manipulated exchanged images. The region-selecting function, performed by a radiologist or a computer aided tool [ 33 ], separates the given medical image into two non-overlapping zones: region of interest ROI and region of non-interest RONI.
The ROI zone contains the significant information that the physicians utilize for diagnosis. Therefore, this region may not be used for watermark embedding in order to preserve its integrity and to prevent any compromise on the diagnostic value of the image.
Since the RONI zone does not contribute to diagnosis, its integrity does not need to be preserved and thus it can be used for the insertion of robust watermarks. The size and shape of the two regions vary according to the modality and nature of the medical image. For effective watermarking, the segmented image is transformed into the frequency domain using a 1-level discrete wavelet transform DWT.
According to this mapping procedure, the ROI coordinates in each sub-band are derived from the spatial domain ROI coordinates based on the spatial self-similarity between the sub-bands. Multiple watermarks are generated to address the different security requirements of medical image transmission. Two watermarks are used to authenticate the ownership and source of origin of the image, and a cryptographic hash watermark is used to verify the strict integrity of the ROI of the image.
The three watermarks and their pre-assigned embedding locations are described below. The 19,bit robust watermark serves for image ownership authentication and is embedded in the LH sub-band. The bit robust watermark is sued to authenticate the source of origin of the image, and it is embedded in the HL sub-band.
The watermark is used to verify the strict integrity of the ROI of the image, and is embedded in the HH sub-band. The proposed watermarking algorithm consists of three procedures: watermark embedding, watermark extraction, and integrity verification procedures. The first procedure embeds the authenticity and integrity watermarks into the RONI, while the second extracts the watermarks from the same region at the receiving end. The third procedure verifies the integrity of the received image, and detects tampered blocks in the ROI of the image.
The embedding procedure inserts the bit-patterns of the three watermarks in the RONI of each sub-band according to the following assignment: the patient information watermark in the LH sub-band, the hospital logo watermark in the HL sub-band HL, and the hash watermark in the HH sub-band.
The operational steps of the procedure are depicted in Fig. The LSB substitution is done by taking the integer value of S Bi 0,0 , preserving the fraction, placing the watermark bit at the LSB position of the integer, and adding the preserved fraction to the modified integer.
The proposed algorithm is blind in the sense that it does not require the original medical image in the extraction process. The procedure is shown in Fig. The physicians at the receiving side have the option of verifying the integrity of the ROI as a whole strict integrity , or by verifying the integrity of the ROI on block-by-block basis localized tamper detection.
The integrity verification steps are described below. Compute the difference between the maximum and minimum pixel values within each decrypted ROI block. Compare the difference value computed for each block against some threshold, empirically found to be , which corresponds to half the maximum possible pixel value. If the difference between the maximum and minimum pixel values within the block exceeds the preset threshold, then the block is considered tampered.
A large set of 8-bit gray-scale medical images have been used to evaluate the performance of the proposed algorithm. Performance results with respect to imperceptibly, robustness, localized tamper detection, and data payload are presented in the following sub-sections.
Benchmark medical images with ROIs shown in polygons. A visual subjective comparison between the original images, shown in Fig. Watermarked benchmarked medical images. MRI image, b. X-ray image. However, since the PSNR metric is not an ideal objective evaluation metric, we believe that the subjective evaluation we have done to evaluate the quality of the watermarked images, alongside with the reasonably high PSNR values we obtained, demonstrate the imperceptibility exhibited by the proposed algorithm.
The transmitted medical images may undergo modifications by different types of signal processing operations. This may affect their perceived quality and corrupt the watermarks embedded within their RONIs. Therefore, we evaluated the robustness provided by the proposed algorithm against several signal processing operations: additive Gaussian noise, additive salt and pepper noise, and JPEG compression.
The robustness is evaluated using the normalized correlation factor which measures the similarity between the original and extracted watermarks. The patient information and hospital logo watermarks can be faithfully used to authenticate the ownership and source of origin of the image, and the hash watermark to verify the strict integrity of the ROI of the image. Similar results have been achieved for the X-ray and ultrasound images.
The proposed algorithm achieves content-based integrity of the transmitted image using a tamper detection and localization scheme. A block is considered tampered if the decryption process fails to restore the block to its original state. This is by virtue of the avalanche effect inherent in AES-CBS which implies that any slight change in the encrypted block will lead to unsuccessful decryption to the original state of the block.
To show the effectiveness of the tamper localization scheme, we slightly tampered the encrypted ROI by modifying one single bit. To further explore the functionality of the scheme, two distant bits were flipped. As shown in the table, the blocks to which the bits belong were not decrypted correctly.
As mentioned earlier, this encryption-based scheme provided tamper localization as well as ROI confidentiality, thus achieving two main requirements of secured telemedicine. According to the embedding capacity equation given below, larger images, smaller block size, and higher DWT levels will provide higher embedding capacity. It is instructive to note here that the capacity equation has been derived in such a way that capacity calculation is confined to three sub-bands LH, HL, HH , since sub-band LL has been excluded from watermark embedding.
ROI capacities are not included in the table since the ROI of the image is not watermarked in the proposed algorithm. The capacity of a given medical image can be further increased to accommodate larger watermarks by partitioning the original image into blocks with smaller size. The capacity gain is due to the fact that one single bit only is embedded in each block regardless of its size as we have described in the previous section.
In this sub-section, a performance comparison is carried out between the proposed algorithm and other region-based algorithms reported in the literature. The comparisons are made with crypto-watermarking, pure watermarking, and pure cryptographic-based algorithms. A few region-based crypto-watermarking algorithms with tamper localization functionality have been proposed the in literature [ 20 , 27 — 30 ].
One major drawback of the proposed algorithms is the extensive use of cryptographic watermarks, such as CRC and hash codes, to implement the tamper localization functionality. Other than being computationally intensive, the algorithms provide no evidence that these cryptographic watermarks were extracted intact at the receiver side. Since a 1-bit change in a CRC or hash code will lead to a false localized tamper detection, extensive use of such cryptographic watermarks is considered a major limitation of the proposed algorithms.
Moreover, the robustness of the proposed algorithms was not evaluated properly using standard metrics such as normalized correlation and bit error rates to prove that the cryptographic watermarks could survive attacks such additive Gaussian noise and JPEG compression. On the other hand, our encryption-based tamper localization scheme offers confidentiality for the ROI of the image in addition to the accurate localized tamper detection rates.
Another limitation in the proposed algorithms is their inefficient ROI recovery schemes. This is by virtue of the fact that the recovered ROI is far from being identical to the original ROI, and thus it may not be appropriate for diagnostic purposes [ 35 ]. Similarly, lossless compression, which has been used by some algorithms, may allow for exact recovery of the ROI of the image; however, the time spent in compressing and decompressing the ROI watermark will introduce a computational overhead that will limit its usability.
Furthermore, the size of the ROI varies from one modality to another, and thus it is not always guaranteed that the RONI will be large enough to accommodate the compressed ROI watermark. For these obvious limitations, the recovery feature has not been incorporated in our proposed algorithm.
The proposed algorithm can be compared with pure watermarking methods such as the scheme described in [ 14 ]. This scheme provides authenticity to the transmitted medical image using a method similar to the method described in the paper; however, integrity and confidentiality are provided differently. As described throughout the paper, the proposed algorithm achieves integrity and confidentially using more effective methods.
The algorithm provides two levels of integrity verification: strict and content-based integrity of the image ROI, and the second by using symmetric encryption to provide confidentiality and tamper localization of the same region. Finally, when compared with the crypto-based DICOM standard, it is important to emphasize that the proposed algorithm achieves confidentiality, authenticity, and integrity of the transmitted image.
Moreover, the digital signature stored in the header of the DICOM image provides authenticity and integrity of the image; however, the signature is susceptible to loss or degradation during compression or transmission, thus it may not be always available for verification. This is a major limitation of the DICOM standard since the security of the header data of the image is as important as the security of its pixel data of the image. In this paper, we proposed a crypto-watermarking algorithm capable of providing secured exchange of medical images between healthcare entities.
The uniqueness of the proposed algorithm is of twofolds: providing strict and content-based integrity of the ROI of the image, and using symmetric encryption to provide confidentiality and tamper localization for the ROI. Performance of the algorithm was evaluated using gray-scale medical images of different modalities with respect to imperceptibility, robustness, capacity, and tamper localization.
The results showed the effectiveness of the algorithm in providing the desired security requirements of telemedicine applications. Our future research will focus developing new watermarking algorithm to handle multi-slice and multi-frame medical images. Ahmad Mohammad, Email: oj. National Center for Biotechnology Information , U. Journal List J Digit Imaging v. J Digit Imaging. Published online Aug Author information Copyright and License information Disclaimer.
Corresponding author. This article has been cited by other articles in PMC. Abstract Telemedicine is a booming healthcare practice that has facilitated the exchange of medical data and expertise between healthcare entities. Introduction Digital information systems have been increasingly deployed in modern healthcare environments in the last decades. Literature Survey A few region-based medical image watermarking algorithms with tamper localization functionality have been proposed in literature.
Open in a separate window. DWT Sub-band Decomposition For effective watermarking, the segmented image is transformed into the frequency domain using a 1-level discrete wavelet transform DWT. Watermarks Generation and Assignment Multiple watermarks are generated to address the different security requirements of medical image transmission. Watermarking Procedures The proposed watermarking algorithm consists of three procedures: watermark embedding, watermark extraction, and integrity verification procedures.
Watermark Embedding Procedure The embedding procedure inserts the bit-patterns of the three watermarks in the RONI of each sub-band according to the following assignment: the patient information watermark in the LH sub-band, the hospital logo watermark in the HL sub-band HL, and the hash watermark in the HH sub-band. Step 1. Apply the SVD operator on block B i.
Watermark Extraction Procedure The proposed algorithm is blind in the sense that it does not require the original medical image in the extraction process. Step 3 Sub-band Partitioning Partition each sub-band into non-overlapping blocks, as shown in Fig.
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Many cryptocurrencies, like Bitcoin, may not explicitly use sending of such secret, encrypted messages, as most of the information that involves Bitcoin transactions is public to a good extent. However, there is a new breed of cryptocurrencies, like ZCash and Monero , which uses various forms of cryptography encryption to keep the transaction details secure and completely anonymous during transmission.
Some of the tools that were developed as a part of cryptography have found important use in cryptocurrency working. They include functions of hashing and digital signatures that form an integral part of Bitcoin processing, even if Bitcoin does not directly use hidden messages. Multiple methods exist for encryption in cryptography. It uses the same secret key to encrypt the raw message at source, transmit the encrypted message to the recipient, and then decrypt the message at the destination.
The above is one of the simplest examples of symmetric encryption, but lots of complex variations exist for enhanced security. This method offers advantages of simple implementation with minimum operational overhead, but suffers from issues of security of shared key and problems of scalability. The second method is Asymmetric Encryption Cryptography , which uses two different keys — public and private — to encrypt and decrypt data.
This method helps achieve the two important functions of authentication and encryption for cryptocurrency transactions. The former is achieved as the public key verifies the paired private key for the genuine sender of the message, while the later is accomplished as only the paired private key holder can successfully decrypt the encrypted message.
The asymmetry used for Bitcoin keys is called elliptical curve cryptography. The specific method is known as secpk1 and was apparently chosen by Satoshi for no particular reason other than it was available at the time! The third cryptography method is Hashing , which is used to efficiently verify the integrity of data of transactions on the network. Additionally, Digital Signatures complement these various cryptography processes, by allowing genuine participants to prove their identities to the network.
Multiple variations of the above methods with desired levels of customization can be implemented across various cryptocurrency networks. Anonymity and concealment is a key aspect of cryptocurrencies, and various methods used through cryptographic techniques ensure that participants as well as their activities remain hidden to the desired extent on the network.
Since each individual's situation is unique, a qualified professional should always be consulted before making any financial decisions. Investopedia makes no representations or warranties as to the accuracy or timeliness of the information contained herein. As of the date this article was written, the author owns no cryptocurrencies. Satoshi Nakamoto. Accessed June 24, Bitcoin Forum.
Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. The "Crypto" in Cryptography. How Does Cryptography Work? Cryptographic Methods Used. The Bottom Line. Key Takeaways Bitcoin and other blockchain-based cryptocurrencies rely on cryptographic methods to maintain security and fidelity - putting the "crypto-" in the name.
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