Demystifying Emerging Trends in Green Technology

Trust-Based Neighbor Selection Protocol to Elect Leader in Blockchain using zk-SNARKs Algorithms

Satpal Singh¹, *, Subhash Chander²

¹ Department of Computer Science, Punjabi University, Patiala, India

² Department of Computer Science, University College Jaito, Jaito, India

Abstract

Blockchain stores and writes all the transactions because of the unlimited storage capacity. Leader election is the process of electing a node as an overall in-charge of the distributed network. Leader election is a complicated task as we have to choose a leader by giving equal opportunity to all the nodes. We implement all the algorithms of the DONS protocol in order to elect a leader but in our TBNS (Trust Based Neighbor Selection) protocol, we add zk-SNARKs proof to enhance the security of Blockchain. zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) is a type of proof used in cryptography to prove the authenticity of information without revealing any additional information. It allows one party to prove to another that they know a certain piece of information without actually revealing the information itself. In the end, the results of our proposed model are compared with RTT-NS and DONS.

Keywords: Blockchain, TBNS, zk-SNARK.

* Corresponding author Satpal Singh: Department of Computer Science, Punjabi University, Patiala, India; E-mail: [email protected]

INTRODUCTION

Blockchain is a distributed ledger that can continuously grow and store a large amount of data. Mining is the process to append a new block onto the chain and this task is completed by the miners. Many nodes in the network compete with each other in order to mine a block and get the reward [1]. We already go through a lot many consensus algorithms i.e. PoW, PoS, PoC, etc. that help us deduce a miner. However, these algorithms are complex, time-consuming, and waste resources because a lot of resources, energy, time, and efforts are required by the users to find out the validator or miner [2-4].

In our proposed TBNS model, blockchain stores and writes all transactions because of the unlimited storage capacity. Instead of deducing a miner by implementing complex algorithms like PoW, PoS, PoET, etc., we go for a leader election process. Leader election is the process of electing a node as an overall in-charge of the distributed network [5-8]. Leader election is a complicated task as we have to choose a leader by giving an equal opportunity to all the nodes. We have implemented all eight algorithms of the DONS protocol but with enhanced protection of zk-SNARKs algorithm. It allows one party to prove to another that they know a certain piece of information without actually revealing the information itself [9, 10]. This type of proof is often used in blockchain systems to improve privacy and security. By using zk-SNARKs, a blockchain system can ensure the authenticity of transactions without revealing the details of those transactions to unauthorized parties [11-13].

The contributions of this study are as follows:

Proposal of TBNS Protocol: The study introduces the Trust-Based Neighbor Selection (TBNS) protocol for leader election in blockchain networks. Unlike traditional consensus mechanisms such as PoW, PoS, and PoET, TBNS focuses on a leader election process that provides an equal opportunity to all nodes, aiming for a more efficient and resource-saving approach.

Integration of zk-SNARKs for Enhanced Security: The TBNS protocol incorporates zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) to enhance the security of the blockchain network. This cryptographic proof allows for the verification of information authenticity without revealing any additional information, thereby improving privacy and security.

Comparison with Existing Protocols: The study compares the performance of the TBNS protocol with existing protocols like DONS (Dynamic Optimized Neighbor Selection) and RTT-NS (Round Trip Time Neighbor Selection). The results show that TBNS outperforms these protocols in terms of finalization time, demonstrating its effectiveness and efficiency.

Implementation and Validation: The algorithms and procedures for leader election using TBNS and zk-SNARKs are implemented in Python, providing a practical validation of the proposed model. This implementation ensures that the theoretical aspects of the protocol are backed by empirical results.

Detailed Protocol Steps and Algorithms: The study provides a comprehensive set of algorithms (Algorithm 1 to Algorithm 8) that detail the steps involved in the TBNS protocol, including message handling, local record checking, request for confirmation, local network view computation, voting, leader recognition, network topology establishment, and optimized neighbor selection. This detailed approach ensures that the protocol is transparent and reproducible.

Proposed Model

Baniata et al. proposed DONS (Dynamic Optimized Neighbor Selection) for smart Blockchain, which initially requires the full view of the blockchain network to compute MST. Also, as per network dynamicity, nodes joining and leaving the network must be updated in this view. The network view will be shared with the leader based on which MST derived by the leader in polynomial time will be broadcasted in the network. The network nodes are only able to read their own and neighbor ID’s. Therefore, no network node can deduce the miner’s private data, also no node has information about the elected leader unless the node itself is a leader.

Problem in Leader election: The Leader node is also a blockchain node with similar characteristics as of other nodes like failure, unavailability, and attacks. So, the Leader election will be performed in the way explained below:

Step I: Network initialization

Initially all the nodes in the network know the identity of their neighbors and expected round trip time (ERT), when communicating with nodes. Also in the beginning, all the node parameters are set to default parameters of the network or protocol as node_status = normal_node.

confirmation_required = equal or higher than the average nodes (50%)

current_Leader = [Null], MST= [], Round_time =T.

Step II: Network view

Once a node or miner fails to join the network i.e. network view is fixed then the process for Leader election is initialized. When the process ID is initialized, the default node status as in Step I, is updated to probable leader.

node_status= probable_Leader

Broadcast message to elect the Leader that contains:

Timestamp (t) \\ imported from step I

Votes= Dict{}, nodes_list(NL) = List[]

Message initialized and broadcasted.

Step III: Check node failure

All the nodes that receive the message as in step II will run algorithm I i.e. Leader election. The rest normal nodes will run algorithms II, III, and IV.

Step IV: ANV (Anonymous Network View)

Once network nodes receive the required LE_msg with all the conditions as in algorithms II, III, and IV then neighbors act as the witnesses for the node(i) joined or left the network. The protocol then selects one random node with the highest hash value and then LE_msg2 is broadcast to all the neighbors. The message contains node IDs and the content of message 1 and 2. Then ANV can be obtained by running algorithm 4.

Step V: Network Leader Declaration

When any node that is elected as a probable leader receives LE_msg2 then it initializes algorithm 5. This is used to save all node votes and ANV. When,

Current_time – t <= T then

Initialize algorithm 6.

This will compute all the received votes and change the node status to Leader or normal node from probable leader according to the votes.

Step VI: In this step, when a probable leader node receives sufficient votes to be a leader, the network topology is constructed and its MST is computed and broadcasted to nodes as in algorithm 8.

Step VII: All the nodes receive LE_msg3, it verifies whether the message is generated by the leader or not; then every node that receives the MST message as in algorithm 8 will derive its neighbors to share and transmit data.

We implement all the algorithms of the DONS protocol in order to elect a leader but in our TBNS (Trust Based Neighbor Selection) protocol, we add zk-SNARKs proof to enhance the security of Blockchain. zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) is a type of proof used in cryptography to prove the authenticity of information without revealing any additional information. It allows one party to prove to another that they know a certain piece of information without actually revealing the information itself. This type of proof is often used in blockchain systems to improve privacy and security. By using zk-SNARKs, a blockchain system can ensure the authenticity of transactions without revealing the details of those transactions to unauthorized parties [11-13].

Algorithm 1 Message Handler with zk-SNARKs.

Algorithm 2 Check local records.

Algorithm 3 (RFC) Request for confirmation.

Algorithm 4 Local Network view.

Algorithm 5 Voting with zk-SNARKs.

Algorithm 6 Leader Recognition with zk-SNARKs.

Algorithm 7 Network Topology with zk-SNARKs.

Algorithm 8 Optimized Neighbor Selection from MST with zk-SNARKs proof.

Complexity: The overall complexity of the TBNS protocol is dominated by the MST construction and neighbor selection processes, which are O (N^2). Therefore, the computational complexity of the entire TBNS protocol with zk-SNARKs is O (N^2).

The analysis assumes that zk-SNARKs verification and proof generation are efficient, typically O(1) for each proof, but the actual performance may vary based on the specific cryptographic implementation and optimizations used.

Results

We have implemented TBNS in python and compared the outputs of Dynamic Optimized Neighbor Selection (DONS) and Round Trip Time Results (RTT-NS) protocols with our proposed Trust Based Neighbor Selection (TBNS) protocol. Clearly, results produced by Trust Based Leader Election protocol are far better than DONS and RTT-NS in terms of finality time (Fig. 1).

Fig. (1))

Flow chart of proposed algorithm.

Conclusion and Future Scope

Mining is the process to append a new block onto the chain and this task is completed by the miners. Many nodes in the network compete with each other in order to mine a block and get a reward. We already go through a lot many consensus algorithms i.e. PoW, PoS, PoC, etc. that help us deduce a miner. Instead of deducing a miner by implementing complex algorithms like PoW, PoS, PoET etc., we go for a leader election process. We have implemented all eight algorithms of the DONS protocol but with enhanced protection of the zk-SNARKs algorithm. The results of our Trust Based Leader Election protocol are better than DONS and RTT-NS in terms of finality time.

The proposed TBNS (Trust Based Neighbor Selection) protocol with zk-SNARKs for leader election in blockchain networks opens several avenues for future research and development:

Enhanced Security Mechanisms

Quantum-Resistant Algorithms: As quantum computing evolves, incorporating quantum-resistant cryptographic algorithms to secure zk-SNARKs and overall blockchain protocols becomes crucial.

Multi-Factor Authentication: Integrate multi-factor authentication for nodes participating in leader elections to further enhance security.

Energy Efficiency

Energy-Efficient Consensus Algorithms: New consensus algorithms must be researched and developed,which require less computational power, making the blockchain network more environmentally friendly.

Green Blockchain Initiatives: Renewable energy sources and carbon offset mechanisms must be incorporated to reduce the environmental impact of blockchain operations.

REFERENCES

Electronic Healthcare Data Security Using Blockchain

Sharda Tiwari¹, *, Namrata Dhanda¹, Harsh Dev²

¹ Department of Computer Science & Engineering, Amity University, Uttar Pradesh, India

² Department of Computer Science & Engineering, Pranveer Singh Institute of Technology, Kanpur, Uttar Pradesh, India

Abstract

In this paper, a blockchain-based healthcare security system as a solution for preventing data forging is proposed. The proposed model for healthcare data security is based on blockchain and its smart contract execution in a secure way. We proposed a system where users can access the open surveys and participate in these surveys. Moreover, their answers cannot be changed by anonymous or 3rd party people with smart contract control. At the same time, to ensure the confidentiality of the patient’s data, we kept the hashed value of the information we collected from the patient’s survey data as evidence and the encrypted version so that data can be used for the evaluation, in the relevant storage units of the generated decentralized application.

Keywords: Component, Formatting, Insert (key words), Style, Styling.

* Corresponding author Sharda Tiwari: Department of Computer Science & Engineering, Amity University, Uttar Pradesh, India; E-mail: [email protected]

INTRODUCTION

Technology is quickly advancing every day. Many tasks were previously completed physically in real life and then shifted to the digital workflow because of this advancement [1-5]. For instance, technology has an impact on finance, trade, multimedia, and advertising. At the same time, another area affected by these technological developments is the health field. Especially with the spread of the internet, the strengthening of computer technologies, and the learning of technology by people working in the field, most health-related data transactions began to be stored in digital areas. During these transitions, databases were used as the first solution for storing health data. As a result of this transition, the amount of data stored electronically has increased day by day. Due to both speed and memory requirements, distributed database structures were established. These

accomplishments and transitions have been very beneficial for healthcare and many other sectors [6-10]. However, it is an undeniable fact that there are parts of technology that cause difficulties in the field of health, as in every field. The most concerning subject among these challenges is the security of healthcare data. The medical operations and their results may contain sensitive or private data that is not desired to be captured or exposed by others. Therefore, ensuring the security of healthcare data is significantly necessary. Due to the necessity of providing security, many studies have been achieved about e-healthcare data protection, since the beginning of healthcare data processing in digital areas. The solution to the security concerns relies on the fundamentals of cryptography as confidentiality, integrity, and authentication. Many techniques have already been developed to provide these cryptographic fundamentals. These security problems can be eliminated with the combination of cryptographic techniques such as authorization control, encryption, masking, anonymization, etc. [11-15].

Research Motivation

The motivation behind the research is the need to revolutionize healthcare systems and improve patient outcomes.

One of the main motivations is the potential to detect diseases at an early stage. Early disease detection can lower healthcare expenditures and significantly improve treatment outcomes [16-20].

Another motivation is the ability to remotely monitor and manage patients' health conditions. Remote monitoring allows healthcare providers to track patients' health status in real time, enabling early intervention and personalized care plans. Additionally, IoT Based Disease Prediction systems can contribute to the development of personalized medicine [21-23].

A further driving force behind research has been the need to solve issues with existing healthcare systems, like excessive wait times, restricted access to care, and underutilization of available resources. IoT Based Disease Prediction systems have the potential to overcome these challenges by providing timely and accurate health monitoring, early detection of diseases, and efficient resource allocation.

Furthermore, research improved population health management. By utilizing IoT devices and predictive models, healthcare providers can gain insights into population health trends, identify high-risk individuals or communities, and implement preventive measures to reduce the prevalence of diseases.

Research Gaps

While there have been significant advancements in the field of IoT-based Disease Prediction and management using Blockchain technology, there are still some research gaps that need to be addressed.

Integration of IoT devices and sensors: Although IoT devices play a crucial role in collecting real-time health data; there is a need for further research on the integration of a wider range of IoT devices and sensors. This integration will allow for more comprehensive and accurate data collection, leading to better disease prediction and management.

Standardization and interoperability: One of the challenges in implementing IoT Based Disease Prediction systems is the absence of common frameworks and protocols for data interchange and interoperability among various IoT platforms and devices. This research gap highlights the need for developing standardized protocols and frameworks that enable seamless interoperability between IoT devices, ensuring effective data sharing and analysis for disease prediction

Data security and privacy: Another research gap in the field of IoT Based Disease Prediction and management using Blockchain is the need for robust data security and privacy mechanisms. The security and privacy of patient data are becoming more and more important as healthcare data becomes more digital. Blockchain technology can help by enabling the decentralized and unchangeable storage of medical records.

Integration with Electronic Health Records: EHR systems play a significant role in healthcare data management. However, there is a research gap in understanding how IoT Based Disease Prediction systems can effectively integrate with existing EHR systems. This integration would allow for seamless data sharing and analysis between IoT devices and EHR systems, leading to more accurate disease prediction and management.

Scalability and performance: The scalability and performance of IoT Based Disease Prediction systems using Blockchain technology are important considerations. Research is needed to explore ways to optimize the performance of Blockchain-based IoT systems, addressing issues such as transaction processing speed and scalability to handle large amounts of data.

Contribution

Data preservation and data immutability are the main requirements for electronic health record systems. In this paper, a system has been designed that aims to save the e-health record data and their results as evidence. Since proof immutability is the main requirement of these kinds of systems, an immutable ledger mechanism is an ideal solution for this requirement. Therefore, we have designed a system that can work in sync with the blockchain. The proposed model aims to provide a scalable, fast, and secure healthcare-proof system to users. While designing this model, we used cryptographic algorithms that comply with the standards. In addition, we chose to use parameters according to the specified standards for these algorithms. After this design, we propose an interface and works synchronously with the preferred blockchain structure.

Literature Review

Azaria et al. offered a blockchain-based user access control system named MedRec. They used the Ethereum blockchain and its smart contract mechanism. They used nodes with two roles as patient and provider nodes. In MedRec’s solution, there are three smart contract designs. The registrar contract ensures the relation between the Ethereum address and the system user. The summary contract provides a record history for each patient, and the patient-provider contract provides access control between patients and related providers. Rajput et al. proposed a blockchain-based control management system for the patient healthcare data. In Rajput’s system, Hyperledger Fabric was used for the blockchain mechanism. Business logic was implemented with the smart contract such as registering and retrieving data operations. In addition, they used an API connection between the application and the blockchain side. Moreover, they determined access control rules according to the roles of the users in the system for preventing unauthorized user activities on patients, doctors, or staff data. Shahnaz et al. developed a pure blockchain system with role-based authorization for ensuring the privacy of healthcare data. In this system, they used Ethereum and its smart contract mechanism Solidity. In their system, there are two types of smart contracts. The first one is the patient record contract, and the other one is for roles. The first one contains all create, read, update and delete (CRUD) functions such as patient record saving, viewing, grant or revoke access controls. The role contract is predefined via the Open Zeppelin library. Their user definitions are based on only Ethereum users. Therefore, the blockchain provides interaction between smart contracts and users. Xu et al. studied blockchain-based approach to IoT-based healthcare system named Health Chain. They constructed two related chains. The first one is a public blockchain named User chain and the second one is a consortium blockchain named Do chain. The user chain stores the user information data in Unblock. For the confidentiality of the user’s IoT data, they have used AES symmetric encryption. In addition, for storing the encryption key and encrypted user’s IoT data separately, they have used two different transactions. Doc-chain, on the other hand, stores the diagnostics of the related users in their block named D-block. As a consensus mechanism, the User chain has a PoW mechanism. However, they prefer the PoS-based Byzantine Fault Tolerance for the Doc chain. Furthermore, the encrypted data are stored on IPFS in storage nodes. Fan et al. suggested another blockchain-based electronic medical data-sharing solution named MedBlock. In this solution, they constructed a private blockchain. In parallel, they use a hybrid consensus mechanism for reducing resource wasting and improving the network speed. The data security is provided by a signature-based access control protocol. Accordingly, if the user signature is not among the signature collections in their system, user access is blocked. The studies we have mentioned so far are mostly blockchain studies based on providing access control. Li et al. suggested a blockchain-based medical data preservation system with Ethereum. They developed a three-layer application as many solutions have been proposed. These layers are the user layer, the application layer, and the blockchain layer. Briefly, they retrieve the data from the user layer, process, read, or update it at the application layer, and submit the encrypted and hashed data to the blockchain layer. In this process, they used algorithms such as AES and SHA-256 to preserve data confidentiality and integrity. Pavel et al. specified the problem as medical data transferring and proposed a blockchain-based solution to their PoS-based blockchain structure. They used signature-based authorization for the image transfer, retrieval, or viewing.

Proposed model

The proposed model consists of three main parts. The first part is the web application of the project. The web server provides the connection with the database and constructs all transaction data with the Algorand API in Python. In addition, the survey operations are creation, filling, and consent processed by the web application. During these operations, all requested body data are converted into designed back-end class objects. The other one is the blockchain side. On the blockchain, all types of transaction data are sent by the web server via Algorand API. Then the validity of the transactions is checked by the system. In this case, if the sent data is valid then the data is committed to the blockchain. Moreover, on the blockchain side, we use smart contracts and asset technology. The last part is the database module. For each generated survey, we create a new executable decentralized application with a unique ID. With this unique ID, we create a new register in the database and map this register with the smart contract ID. In addition, the survey data such as questions, options, descriptions, etc. are stored in the database. Most of the time, the database operations are quicker than backend data operations. However, due to the cryptographic library methods, database encryption is faster than back-end encryption. Hence, we use a database for sending survey data more quickly to the patient side instead of back-end operations. The overview of the general structure is given in Fig. (1) below.

Fig. (1))

Mechanism for smart contract execution.

The smart contract initially verifies the kind of application transaction when it is performed by any application transaction. A new application will be produced if the application ID is equal to zero. When creating surveys, this control is utilized. There is already an application if it is not equal to zero. The smart contract then determines if the user has authorization after those controls. The user's MST balance is checked in order to implement this control. If the user's MST balance is more than zero, it is successful. If not, this user's smart contract automatically completes. Both the filling out of permission forms and survey requests use this control. The smart contract has now split into two branches. The permission form approval is handled by this one. The other is used to complete surveys. The smart contract is called when the consent form is filled out. The preceding stages are checked once more. If all requirements are met, the application first looks at the kind of smart contract that was invoked. If it is an opt-in kind, it offers a check to see if the user has already registered. In the event that one is given, the smart contract records the argument sent for the consent form as key-value pairs in the user's local storage. Otherwise, the agreement was broken by the smart contract. The cancellation causes the transaction to fail. When filling out a survey, the smart contract is contacted once again and the attributes are checked again. The smart contract checks two parameters if all requirements are satisfied. The first step is to determine whether or not the user is registered. The presence of previously recorded survey data is examined for the second. If the contract breaches the agreement and discovers any registration-related data that has been saved, the transaction will again fail. In the absence of this, the contract accepts the argument given for the encrypted survey responses and hashed survey data, and saves them as key-value pairs in the user's local smart contract storage as in the consent form procedure.

Survey Creation

Once a survey is created, the blockchain and application sides cooperate. The creation of smart contracts and transforming them into blockchain-compatible agreements are the most important aspects of this stage. Each survey generation activates the new decentralized application with a distinct ID in the blockchain. The doctor is the creator of the survey. Therefore, he prepares the survey and sends it to the application side with his mnemonic key. In this step, the system requires survey data in JSON format. Once the survey data reaches the application, it is adapted to the designed object class for processing. Then, the contract transaction creation process is performed. This transaction is named Application Call Transaction. After validation of the 31 blockchain, if there is not an invalid parameter or operation request, the transaction is committed to the system. And our smart contract is executed in the AVM. Otherwise, the transaction fails. In the successful case, we adapt the survey data to the data transfer object. After that, we save the survey information to our database. During registration in the database, we encrypt the survey data with the AES algorithm with the CBC mode (Fig. 2).

Fig. (2))

Mechanism of survey creation.

Consent Form

Medical surveys are widely used in many areas of healthcare. These surveys can be done incrementally, once, continuously, or both before and after some medical operations. The answers or the results of the survey might contain sensitive data. Due to the principle of doctor-patient confidentiality, both doctors and patients avoid the disclosure of this data. Moreover, some tests might have unexpected consequences. Therefore, the consent form is filled out by the patients for the data confidentiality agreements and a disclaimer for unpredictable results. Because of these requirements in the medical surveys, we construct a consent form structure. Since the consent form is an agreement between the doctor and the patient, we store this consent form on the blockchain side for immutable proof of agreement. Fig. (3) explains the generation of transactions and the execution of the smart contract mechanism for the consent form. Once the participant requests to fill out the determined survey, the consent form first appears to the patients before the survey has been sent. The participant reads the terms and conditions. If the patient declines the terms and conditions, he redirects to the main page, and the survey is canceled until he accepts the consent form. Once he confirms the consent form, the consent data is constructed with the server-side functions and generates the designed object class. Then the hash value of the consent data H(C) is generated via the SHA-256 hash function. After this step, the contract transaction generation process begins. The flow chart of consent operation is given in Fig. (3) below.

Fig. (3))

Consent form mechanism.

Survey Completion

The most important part of our solution is survey completion. The participants who signed the consent form and agreed to the terms and conditions are qualified to respond to the survey's questions. The user must confirm the consent form in the previous step after requesting the survey. Once he has access, he may get the survey and begin filling it out. User responses are used to collect the survey data, which is then prepared into the designated survey object structure. The application is split into two branches at this point. The first step entails using the SHA-256 hash method to calculate the hash value of the survey data. The following sentences describe the objective of this operation:

It provides unique proof of user’s answers.

It fixes the size of survey data since the size of the survey data depends on the 35 number of questions and options.

In the final step, the smart contract is executed unless there is no violation of the transaction structure. The mechanism of the contract is given as in the following statement:

It checks user has any MST for the access.

If he has any MST, then it checks user registry status.

If it is satisfied then it stores the hash and encrypted data as key-value pair separately. After the smart contract finishes itself, the whole process is completed.

The overview of the survey filling operation is given in Fig. (4):

Fig. (4))

Survey completion process.

CONCLUSION

The proposed system is efficient in ensuring the healthcare data security. The proposed system tested on sample survey has 10 questions, and each question has 4 options. Therefore, the size of our formatted answer is 39 bytes. Database operations have a fixed size. In this system, average response time of the transaction is 5969 ms with the 319 B memory usage. And their total operation time is between 15 seconds and 18 seconds approximately. Further, the proposed system can be extended with more data along with data learning in future.

REFERENCES

E-cops - An Online Crime Reporting System

Ranvee Kashyap¹, Rohit Kumar¹, Satyam Kumar Nikhil¹, B. Shajahan¹, *

¹ School of Computer Science and Engineering, GB Nagar, Uttar Pradesh, India

Abstract

Transmit information on cutting-edge police and policing tools, create the framework for firms to use in order to obtain incident reports, cut down on employee resources, and enable these enforcement offices to reallocate resources to nearby areas. The E-Cop system is a step toward digitalizing the police station at this time, since everything is going digital. This will bring the public and law enforcement closer together while also enabling communication among all police stations. The E-Cop system allows citizens to report crimes of any kind online to the police station that is closest to them. Every Police Station in this system keeps a general journal, records of crimes and criminal activity, FIRs, and complaints, send the status of complaints, and can view an overview of another police station. Police officers are able to speak with one another. News, notifications, and a plethora of other information are also shown on the system homepage.

Keywords: Criminal, Communication, Digitalizing, Information, Notifications.

* Corresponding author B. Shajahan: School of Computer Science and Engineering, GB Nagar, Uttar Pradesh, India; E-mail: [email protected]

Introduction

"Establishing a data platform that organizations use to receive reports of proximity incidents and provide state-of-the-art police and policing tools [1] will reduce the need for typical assets and enable these authorized offices to reallocate resources to the most desired areas locally." The E-Cop system is a step toward digitalizing the police station at this time since everything is going digital. This will bring the public and law enforcement closer together while also enabling communication among all police stations [2]. The E-Cop system allows citizens to report crimes of any kind online to the police station that is closest to them [3]. Every Police Station in this system keeps a general diary, records of crimes and criminal activity, FIRs, and complaints, sends the status of complaints, and is able to view the general information about another police station [4]. Police officials

can communicate with each general detail of another police station. Police officers are able to speak with one another [5]. Notifications, news, and a plethora of other information are also displayed on the system's home page [6]. The primary goals of E-Cop are adherence to the Indian Constitution, upholding dignity, compassion, and decency toward citizens, and preserving institutional and personal credibility, [7]. and enforcing law and order equitably and in good faith in order to provide safe and secure protection for all societal segments [8]. Particularly the most vulnerable and defenseless groups are women, youth, the elderly, and minorities [9]. Additionally, we want to maintain racial consistency, recalling popular beliefs based on the majority in our constitution [10], adopting a group-inclusive approach [11]. Human resources enable us to prevent wrongdoings and maintain public control so that we can move forward to assist the public truly.

Related Works

A more robust online crime reporting system is required due to the rising incidence of crimes involving the Internet [12]. Such an online system, called e-cops, is intended to be a useful tool for reporting any kind of illegal activity. E-cops have potential, but it also has drawbacks [13]. The creation of precise diagnostic models is one of the E-cops' main challenges [14]. These models must enable a smooth user experience and are crucial for assessing the probability of a specific criminal event. One of the primary challenges in creating diagnostic models for E-cops is managing changeable data [15]. Different kinds of data are linked to every type of internet crime [16]. E-cops need to be able to recognize and categorize the data points linked to the reported crime in order to estimate the chance that [17] it will happen again. Additionally, for data to continue being accurate and useful, it must be updated [18]. Secondly, the difficulty of forecasting future criminal activity must be considered while creating diagnostic models for E-cops [19]. Apart from historical trend analysis, more study is required for E-cops models to be effective; advanced techniques for predicting the probability of future events are needed [20]. This calls for an intricate fusion of machine learning data analysis.

Overview

An online tool for reporting crimes, called E-Cops, was created to make the process of reporting crimes safe and effective. It makes it possible for citizens to swiftly, simply, and safely report criminal incidents online. It gives people a safe place to keep, submit, and retrieve their crime reports. Additionally, it gives the public access to information like the incident's timing and location. In addition, E-Cops acts as a central location for all citizen-submitted crime reports. Law enforcement organizations receive access to all of the data gathered in order to support their criminal investigations. E-cops provide a plethora of features to guarantee safe and convenient crime reporting. These capabilities include real-time case monitoring, user authentication, safe criminal data storage, and customized report layouts. Law enforcement organizations are able to investigate and settle crimes more quickly and effectively thanks to E-Cops, which also helps in improving the reporting of crime situations safely and efficiently.

PROCESS METHODOLOGY

Planning: Setting up the system is the initial phase in the process. In order to do this, requirements must be gathered, goals must be determined, a budget and schedule must be decided upon, and an implementation strategy must be created. Fig. (1) shows the Online Crime Reporting System.

Fig. (1))

Shows online crime reporting system.

Design

Following the completion of the planning stage, the design stage starts. Here, the system's architecture, data modelling, and user interface design are all studied in depth.

Development

The system needs to be put into action now. The system must be programmed, tested, and deployed during the development phase.

Data Collection

Data collection aims to provide state-of-the-art law enforcement and policing tools, knowledge platforms that companies can utilize to analyze incident reports, save employee costs, and enable these implementation offices to reallocate resources to areas that are highly anticipated in the local community.

Data Preprocessing

For businesses to operate accessible work without any problems, they require an effective program and the required equipment. As of right now, day one underlying NICs are structurally accessible, meaning that buying them does not cost extra. Our pledge is thus financially feasible.

Data Analysis

Given that it does not affect the different apps that use the framework, this is functionally feasible. Despite this, network traffic is not as negatively impacted. Every frame in the company needs to have it installed.

Results and Analysis

According to the findings of a study conducted on E-cops, an online crime reporting system, users find the system to be user-friendly and helpful in reporting crimes swiftly and efficiently. Total 31% of users rated the system as very good, while the bulk of users (69%) rated it as great. The consumers also mentioned how simple it was to use the system, how straightforward the user interface was, and how quickly a report could be filed. Users also expressed confidence that their reports would be handled appropriately and swiftly. Fig. (2) shows the global geography of cybercrime.

The findings point to the E-cops system being a functional online tool for reporting crimes. Users found it to be speedy, easy to use, and confident in the framework. The findings have ramifications for improving the speed and accuracy of crime reporting as well as for improving efficiency.

Fig. (2))

Shows the global geography of cybercrime.

Conclusion

E-cop is the name of the internet application business. The program can reveal complaints, missing persons, and web-based infractions in addition to providing the most necessary personal information. Adaptability was considered in the creation of this product. When more modules are needed, they can be added with ease. The product is made using a certain technique. Every module inside the framework has been tested with both legitimate and invalid data, and everything functions as it should. As such, the framework can take the place of the existing framework and meet all of the major objectives. Under the worst possible conditions for the Association's success, the joint venture is essentially finished. The requirements are satisfied and successfully met. The planning of the framework is done as though it were during the planning stage. The mission offers clever suggestions for creating applications that are unquestionable and satisfy client needs. The framework is incredibly flexible and adaptive. Customers will find it easy to use this device because of its easily interpreted screen. Errors are significantly reduced by raised approval checks. A significant redesign of the product is planned. With real-time data, the app tested it out and came out on top. As a result, this product has demonstrated