1. Introduction

2. Materials and Methods

2.2. Tracing Model Operations

2.2.1. Sensing Data Collection

We develop the experiments on the Mobile Health Human Behavior dataset from Kaggle, a prominent online repository for diverse datasets. This dataset comprises body motion and vital signs recordings from ten volunteers of varying demographics performing various physical activities. Sensors positioned on the chest, right wrist, and left ankle captured the movement of different body parts, specifically acceleration, gyroscopic rate of turn, and magnetic field orientation. Data collection employed Shimmer2 wearable sensors. All modalities were recorded at a 50 Hz sampling rate, deemed sufficient for capturing human activity. The activity set encompasses diverse actions, including standing, sitting, relaxing, walking, running, jogging, and more. We consider a subset of these records, generated through sensor data, to be embedded within the NFT image. Figure 3 depicts a sample of these embedded records.

2.2.2. Priming NFTs for Use

Unique NFTs are assigned to users throughout the business ecosystem. Before assignment, multiple steps prepare them for deployment. First, data generation occurs. Each generated NFT holds data for various purposes. In our case, mutable data enables the transfer of sensitive information. This data is carefully reviewed before being generated by each business. Minting NFTs requires a dedicated smart contract. In our scenario, a custom contract is built and deployed. Typically, the user’s original business’s admin unit is responsible for creating, auditing, and verifying this contract. Upon creation, the contract starts assigning NFTs to business users, linking their data to these NFTs. These NFTs with associated data secure users’ sensitive information as they move between businesses. Figure 4 shows the sequential stages through which NFT progresses to achieve the data exchange.

2.2.3. Scenario

A consortium of businesses is establishing a secure data exchange system for their users. Sensitive user data samples, collected via sensors and sourced from the MHEALTH dataset, are stored in a human-readable format, typically a text file. This information is then concealed within an ordinary image $I_{\mathrm{norm}}$ using steganography and creating an encrypted image $I_{\mathrm{enc}}$ with no visible signs of hidden data. Extracting the concealed information requires decrypting $I_{\mathrm{enc}}$ using a password. The NFT creation process enables the incorporation of unique identifiers generated by IPFS, establishing a direct reference to the original data stored within the IPFS system. IPFS (InterPlanetary File System) is a decentralized storage system designed to create a peer-to-peer mechanism of storing and sharing data in a distributed manner. $I_{\mathrm{enc}}$ is then uploaded to IPFS to function as an NFT image. The resulting unique identifier becomes a reference in the NFT metadata file, containing details like name, age, and description, specified by the business. The metadata file is also uploaded to IPFS and the obtained unique identifier is used in the NFT smart contract to link $I_{\mathrm{enc}}$ with the metadata. Each user is assigned an NFT, serving as both an ownership mechanism and a holder of sensitive data for cross-border sharing. To transition to a new business, a user’s descriptive data is embedded within the NFT image for transfer. A smart contract on the Ethereum blockchain controls access, ensuring only authorized administrators can update the NFT’s metadata. The updatable metadata allows changes to the user’s NFT, supporting minting for users to connect with their updated metadata on IPFS using their wallet address. Figure 5 provides the procedures involved in employing NFTs as a means for data exchange.

2.2.4. User Mobility

Our model tackles user mobility across diverse businesses, where user data is concealed within steganographic NFT images. As previously mentioned, accessing this data requires a password. Secure and unaltered transmission of this password is crucial, while also actively involving the user. For this, we leverage the power of One-Time Passwords (OTPs).

The first critical step involves the user’s originating network generating an OTP. This OTP acts as a unique binary key, equal or greater in length to the steganography password. It’s a random binary sequence of equal length, denoted as (K = K[0], K[1], ..., K[L-1]), where (K[i]) represents the (ith) bit. Essentially, the generation process can be summarized as:

$$K\left[i\right]\stackrel{R}{\leftarrow }\{0,1\}$$

(1)

Each bit $K\left[i\right]$ is sampled uniformly and independently from 0,1 using a random process operator $\stackrel{R}{\leftarrow }$, guaranteeing unbiased selection. The fundamental operation for confidentiality is bitwise $XOR$$(\oplus )$, applied to each corresponding bit pair from P and the K. The resulting ciphertext (C) is obtained by applying $(\oplus )$ to each bit, generating a new sequence based on the combined values.

$$C\left[i\right]=P\left[i\right]\oplus K\left[i\right]$$

(2)

where $C\left[i\right]$ represents the $ith$ bit of the ciphertext. The OTP, a one-and-done key, is never used twice, ensuring absolute secrecy. The user receives this key to unlock the steganography password in the receiving business or network when the need arises.

With the OTP safely in the user’s hands, the originating network can send the encrypted password C through any insecure channel such as the internet. Even if compromised, C is just random noise without the user’s OTP. The receiving network is helpless without it as they have the encrypted password, but it means nothing without the user’s decryption code. The user’s exclusive possession of the OTP guarantees password control and security every step of the way, even in the face of potential threats. The following algorithm outlines the password transmission process.

Algorithm 1: Secure Password Transmission

Input: Steganography password P, User’s OTP K Output: Decrypted password P for sensative data access $C_P\leftarrow P\oplus K$; $C_P$→ Transmit (B1 → B2); $B2$← Wait; $P\leftarrow C_P\oplus K$; ⇒ Access sensative data;

Upon secure reception of the ciphertext C by any receiving business, the decryption process requires close collaboration with the user to obtain the steganography password P. This decryption process is detailed as follows:

Each bit $P\left[i\right]$ of P is calculated using a bitwise XOR operation between the corresponding bits of the C and the OTP (K). This operation can be expressed mathematically as:

$$P\left[i\right]=C\left[i\right]\oplus K\left[i\right]\mathrm{for}i=0,1,\dots ,L-1$$

(3)

The validation for the decryption process can be represented as follows:

$$F_{\mathrm{Decryption}}(C\left[i\right],K\left[i\right])=\left\{\begin{array}{cc}\mathrm{Error}\hfill & \mathrm{if}C\left[i\right]\oplus K\left[i\right]\ne P\left[i\right]\hfill \\ P\left[i\right]\hfill & \mathrm{if}C\left[i\right]\oplus K\left[i\right]=P\left[i\right]\hfill \end{array}\right.$$

(4)

The application of the XOR operation serves to reverse the encryption process and retrieve the original password. The following algorithm explains the migration scenario between two different businesses.

Algorithm 2: User Data Migration from B1 to B2

3. Setup and Implementation

4. Results and Discussion

5. Conclusions

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