Elections are an integral part of democracy. With democracy being the most popular government type in todayâs world, the need for the use of free and fair elections to elect a presidential candidate or a party has increased. Elections allow everyone in the country above a certain age group (Age limit as per a country or stateâs norms) irrespective of their gender, caste, color, religion, etc., to take part in election and vote for the candidate or party of their choice. The most popular methods of conducting elections are using ballot paper and electronic voting machines (EVMâs). These methods come with their own set of challenges such as lack of security, inefficiency, bogus voting, increased expenditure and ease of manipulation of the props used for voting. In order to tackle these challenges, we are proposing an implementation of elections by making use of permissioned blockchain technology. We aim to make a decentralized voting system using Hyperledger Fabric framework to establish a permissioned blockchain network which would negate the disadvantages of previously used voting mechanisms out there and restrengthen the true meaning of free, fair and sovereign election. In this paper we have described several use cases explaining the various security measures that could be added in order to improve security of the voting systems. Also, we have made use of Hyperledger Caliper to benchmark our blockchain based voting system and test out the system performance with respect to different chaincodes.
Runkai Yang, Xiaolin Chang, Jelena MiĹĄiÄ, Vojislav B. MiĹĄiÄ Âˇ 5 authors
Fork after withholding (FAW) attack is an easy-to-conduct attack in the Bitcoin system and it is hard to be detected than some attacks like selfish mining and selfholding attacks. The previous studies about FAW attack made some strong assumptions, such as no propagation delay in the network.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Hongyu Zhu, Libo Feng, Jianzhao Luo, Yani Sun ¡ 6 authors
The traditional electronic voting (e-voting) has the problems of dead vote, repetition and missing registration, which cannot reflect the voting result correctly, objectively and fairly. With the booming development of blockchain in recent years, blockchain technology provides a new solution in the field of e-voting. Multi-District election is an important method of election in real life, e.g. in US presidential election. Inspired by the electoral college system, we propose a blockchain-based e-voting scheme for multi-district election. First, we propose a blockchain-based voter registration method that all voters are authenticated by the blockchain system. Second, we design a two-layer blockchain architecture, where the lower layer records the votes of voters in each district and the higher layer records the votes of electors. Then we describe the voting process and evaluate the security and availability of the proposed scheme. The experimental results show that the proposed scheme can satisfy the needs of multi-district election.
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
By nature, a traditional attack method, denial-of-service (DDoS) attack poses a considerable threat to the security of the blockchain network layer. This paper proposes a distributed DDoS-attack traffic detection method based on a cross multilayer convolutional neural network model in the blockchain network layer. The method resolves the low generalisation, high misreporting rate, and low detection efficiency problems of the existing detection methods, which are caused by nondistinctive core features and the high complexity of robust features when detecting DDoS attacks transmitted by mixed protocols on a blockchain network layer. First, the model performs a convolution operation on preprocessed traffic on the blockchain network layer using a cross-layer method based on L2 regularisation. After this operation, the model can perceive the detailed features of attack traffic from multiple levels while enhancing the representational performance of key features; specifically, the parameters with high-variance terms are penalised to limit changes in the modelâs weight parameters. The highly robust abstract features of attack traffic are extracted, thereby increasing the generalisation ability and reducing the misreporting rate of the model. Second, parametric encoding of the abstract features is performed by a stacked sparse autoencoder based on KullbackâLeibler divergence, and the sparsity of the model is adjusted to reduce the redundant data and the coupling between abstract features. The outputs of the encoded features are then effectively categorised. Finally, the global optimisation of parameters is performed by an improved random gradient-descent algorithm, which prevents oscillation of the training parameters and accelerates the model convergence. In an experimental evaluation, the proposed method achieved satisfactory binary- and multiclass detection of DDoS-attack traffic on both CSE-CIC-IDS 2018 on the AWS dataset and on the real mixed data of a blockchain network layer.
The Internet has become one of the most important technologies in the world, and hackers use various methods to launch cyber attacks to profit from it. Phishing is one of famous social engineering attacks, it is often used to steal user data, including login credentials and credit card numbers. Although the Transport Layer Security certificate is used to verify the trust of websites, there are still a series of vulnerabilities. The demand for trusted IP addresses has led a lot of research, including IP whitelisting, DNS filtering and so on. However, these technologies still have many shortcomings. In view of this, we proposed a novel mechanism for verifying websites using blockchain technology. The URL and IP address of a permissioned website are recorded in blockchain through a specific smart contract. A DNS query is executed through a smart contract designed to avoid URL redirection attacks. With the help of immutable nature of blockchian, phishing websites can be detected. The mechanism will not add any load to users and provides tamper-proof functions based on the characteristics of blockchain. The comparison of related works shows that the proposed mechanism is more secure. We also provided a reference implementation of the proposed mechanism on Ethereum Quorum simulation platform, which proves the effectiveness and practicability of the mechanism.
Christian Killer, Moritz Eck, Bruno Rodrigues, Jan von der Assen ¡ 6 authors
Recent years saw an increase in voting systems using public permissionless blockchains. Although public blockchains offer transparency and immutability, permissioned consensus is better suited for voting systemsâ requirements, because an initial level of trust in authorities is always required. Hence, a permissioned Distributed Ledger (DL) immutably storing the voting systemâs audit trail satisfies demands measurably.ProvotuMN 3.0 is a decentralized and receipt-Free (RF) voting system based on an end-to-end verifiable Re-Encryption Mixnet (RMN). RMNs allow for flexible votes and elections and decouple the ballot structure from the cryptographic voting protocol. Thus, ProvotuMN decentralizes trust (i) through the use of cryptographic shuffles and Non-Interactive Zero-Knowledge Proofs (NIZKP) in an RMN executed among DL nodes, (ii) by employing a distributed key generation for election keys, and (iii) by offering a decentralized re-encryption service assuring RF. Performance evaluations performed indicate that the voting scheme is scalable for large-scale voting.
Matthias Grundmann, Max Baumstark, Hannes Hartenstein
A recent spam wave of IP addresses in the Bitcoin P2P network allowed us to estimate the degree distribution of reachable peers. The resulting distribution indicates that about half of the reachable peers run with Bitcoin Coreâs default setting of a maximum of 125 concurrent connections and nearly all connection slots are taken. We validate this result empirically. We use our observations of the spam wave to group IP addresses that belong to the same peer. By doing this grouping, we improve on previous measurements of the number of reachable peers and show that simply counting IP addresses overestimates the number of reachable peers by 15 %. We revalidate previous work by using our observations to estimate the number of unreachable peers.
Due to the transparency of blockchain, adversaries can observe the details of a transaction, and then utilize the amount as a unique quasi-identifier to make deanonymization. Nowadays, to obscure the linkages between receivers and senders within a transaction on the blockchain, mixing services are widely applied in many real applications to enhance cryptocurrencies' anonymity. The basic idea of mixing services is to hide an output within several other outputs in a transaction such that adversaries cannot distinguish them by their amounts since they are purposely selected to have the same amount. For a set of original outputs with different amounts, mixing services need to decompose them into a set of decomposed outputs, where any decomposed output has some other decomposed outputs with the same amount. Since the transaction fee is related to the number of outputs, we are motivated to decompose original outputs into a minimal set of decomposed outputs, which is challenging to guarantee the privacy-preserving effect at the same time. In this paper, we formally define the anonymity-aware output decomposition (AA-OD) problem, which aims to find a c-decomposition with a minimum number of decomposed outputs for a given original output set. A c-decomposition guarantees that for any original output$o$, there are at most$c$of all decomposed outputs with an amount of$x$coming from$o$. We prove that the AA-OD problem is NP-hard. Thus, we propose an approximation algorithm, namely Boggart11Boggart is a magical creature in J. K. Rowling's Harry Potter series who can shift his shape and no one knows what it looks like., to solve the AA-OD problem with a (2/c + 3)-approximation bound on the number of decomposed outputs. We verify the efficiency and effectiveness of our approach through comprehensive experiments on both real and synthetic data sets.
Zero-knowledge blocklists allow cross-platform blocking of users but, counter-intuitively, do not link users identities inter- or intra-platform, or to the fact they were blocked. Unfortunately, existing approaches (Tsang et al. â10) require that servers do work linear in the size of the blocklist for each verification of a non-membership proof.We design and implement SNARKBLOCK, a new protocol for zero-knowledge blocklisting with server-side verification that is logarithmic in the size of the blocklist. SNARKBLOCK is also the first approach to support ad-hoc, federated blocklisting: websites can mix and match their own blocklists from other blocklists and dynamically choose which identity providers they trust.Our core technical advance, of separate interest, is the HICIAP zero-knowledge proof system, which addresses a common problem in privacy-preserving protocols: using zero-knowledge proofs for repeated but unlinakble interactions. Rerandomzing a Groth16 proof achieves unlinkability without the need to recompute the proof for every interaction. But this technique does not apply to applications where each interaction includes multiple Groth16 proofs over a common hidden input (e.g., the userâs identity). Here, the best known approach is to commit to the hidden input and feed it to each proof, but this creates a persistent identifier, forcing recomputation. HICIAP resolves this problem by aggregating n Groth16 proofs into one $O(\log n) -$sized, $O(\log n) -$verification time proof which also shows that the input proofs share a hidden input. Because HICIAP is zero-knowledge, repeated shows of the same aggregate or an updated aggregate are unlinkable even though the underlying Groth16 proofs are never recomputed.
Due to the forecasted fast increasing cellular traffic and the already highly congested licensed spectrum, it is critical to exploit and utilize the unlicensed spectrum resources for the fifth-generation (5G) and beyond networks. A challenging problem is the coexistence of 5G and other networks with fair, reliable, and efficient sharing of the unlicensed spectrum. In this paper, we propose a blockchain-enhanced distributed spectrum sharing scheme for coexisting multiple operators and multiple WiFi APs. We design a novel lightweight and efficient consensus mechanism, named Proof of Strategy (PoG). In this consensus mechanism, the problem of spectrum sharing is used as a consensus puzzle, and the part of the unlicensed spectrum is used as the âfeeâ of miners. With such a design, the computing overhead of the consensus process is expected to be reduced significantly. We develop a non-cooperative game to analyze the behavior of the miners and obtain a symmetric Bayesian Nash equilibrium under the uniform distribution of mining cost estimation. It can be found mathematically and experimentally that the strategy of the winner tends to maximize the system revenue by sharing the unlicensed spectrum resource. Furthermore, to reduce the impact of heavy interactions on system throughput, the operation of WiFi APs in the proposed scheme can be adaptively switched between âcontention modeâ and âblockchain modeâ according to the network traffic load. The dynamic behavior is constructed as an evolutionary game, and the existence and uniqueness of equilibrium points are proved by theoretical analysis. Simulations demonstrated the fairness and effectiveness of the proposed blockchain-based scheme and the mode switching method for distributed spectrum sharing by heterogeneous wireless networks.
Blacklists are a widely-used Internet security mechanism to protect Internet users from financial scams, malicious web pages, and other cyber attacks based on blacklisted URLs. This demo introduces PhishChain, a transparent and decentralized system for blacklisting phishing URLs. At present, public/private domain blacklists, such as PhishTank, CryptoScamDB, and APWG, are maintained by a centralized authority, but operate in a crowd sourcing fashion to create a manually verified blacklist periodically. In addition to being a single point of failure, the blacklisting process utilized by such systems is not transparent. We utilize the blockchain technology to support transparency and decentralization, where no single authority is controlling the blacklist and all operations are recorded in an immutable distributed ledger. Further, we design a page rank based truth discovery algorithm to assign a phishing score to each URL based on crowd sourced assessment of URLs. As an incentive for voluntary participation, we assign skill points to each user based on their participation in URL verification.
Digital voting is the use of electronic device, such as voting machines or an internet browser to cast vote. It has disadvantages like security of data and potential attacks such as DoS and DDoS attack. One way to solve these security credentials problems is through the blockchain technology. Blockchain technology embraces a decentralized system and the entire database is owned by many users that overcome the disadvantage of centralized system. The existing system has many security, confidentiality, and anonymity issues. These issues can be resolved by using blockchain technology. Here in the proposed system Ethereum blockchain is used, which enables smart contracts. Smart contracts can be written with solidity, which is object oriented programming language. The outcome of the project is it provides privacy by keeping individualâs vote secret by providing a unique transaction id to each voter. Eligibility is made once to vote and the transaction will be highly secure and encrypted.
Kun Li, Huachun Zhou, Zhe Tu, Feiyang Liu ¡ 5 authors
The malicious flow originating from massive access devices in 6G network will increase sharply. In order to effectively reduce malicious flow, we hope to establish a new framework for coordination of security monitoring and malicious behaviour control in 6G network. Federated learning provides data and privacy protection for the distributed network security behaviour knowledge base. However, since the equipment of its participants needs to upload the original data to the central server for model training, this may lead to data leakage in the knowledge base. Therefore, in this article, we first use the knowledge graph to describe network security behaviours, then build a universal network security malicious behaviour knowledge base, and discuss its application scenarios. Then, we propose a blockchain empowered federated learning (BeFL) for distributed network security malicious behaviour knowledge base architecture to ensure the security of knowledge transmission. Finally, we deployed the designed distributed knowledge base in the prototype system and compared it with the other two baseline methods to verify the performance. Relevant results show that our method outperforms other methods in terms of user identification, flow detection, and attack source tracing.
A. Sampathkumar, Shishir Kumar Shandilya, NebojĹĄa BaÄanin
In the area where privacy is of greater concern, federated learning,a distributed machine learning strategy for preserving privacy,is widely employed in several privacy concern applications. In the meantime, neural architectures became familiar with deep learning approaches for automatic tuning of the architecture of deep neural networks (DNN). While searching with neural architecture and federated learning has experienced several challenges, optimized neural architecture research in federated learning is extensively on demand. DNN faces numerous issues while training such user privacy and ensuring the integrity of the aggregated results obtained from a server. To provide solutions for the above-mentioned issues, enormous federated learning techniques worked towards preserving privacy and were applied in different situations. Still, it is an open challenge that enables users to verify if the cloud server functions appropriately while ensuring usersâ privacy while training. Federated Learning Method is a new way to improve the accuracy and precision, since the previous approach failed to opt the solutions. Here, Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL)is proposed to ensure the confidentiality of usersâ local gradients while performing federated learning. The parameters such as classification accuracy, running time, Communication overhead, Computation overhead, and transaction speed are considered. The values obtained for these parameters are compared against three standard methods, namely Biparing Method (BM) Homomorphic Cryptosystem (HC), and Multiple Authorities with Attribute-Based Signature scheme (MA-ABS)against proposed Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL). As a result, the proposed ECC-BFL achieved 95% of classification accuracy, 65 sec of running time, 76% of communication overhead, 63% of computation overhead, and 92% of transaction speed.
There is a rising concern about authorization in IoT environments to be appropriately designed and applied, due to smart things surge to be part of people's daily lives on one hand, and the amount of personal/private information they utilize, on the other hand. Different access control systems have been proposed for different IoT environments, many are remaining only at a conceptual level. In this paper, we propose a decentralized, ledger-based, publish-subscribe based architecture for the administration of access in a smart home IoT environment to preside at the assignments of underlying operational authorizations. Proposed architecture is endorsed by a proof-of-concept implementation, which utilizes smart contracts to ensure the integrity of administration supplemented by intrinsic benefits of blockchain to be distributed and transparent. Despite the rising hype around the blockchain technology that stokes its utilization in different domains, utilizing it for access control purposes is not yet promising. Our implementation results assure using blockchain for administrative access control is propitious, while is not yet appropriate for operational access control, which have been mainly the focus of previously proposed blockchain-based access control works.
Software-Defined Networking (SDN) enhances the flexibility and programmability of networks by separating control plane and data plane. The logically centralized control mechanism makes the control plane vulnerable in both single and multiple controller scenarios. Malicious third parties can exploit vulnerabilities of reactive forwarding mode to launch distributed denial-of-service (DDoS) attacks against SDN controllers. Unfortunately, existing DoS/DDoS solutions under single controller can not afford effective performance under multiple controllers due to the absence of cooperative detection and mitigation. To solve the above problem, we propose a blockchain-based SDN-targeted DDoS defense framework (BSD-Guard) that can provide cooperative detection and mitigation mechanism to protect SDN controllers. BSD-Guard introduces a blockchain-based secure middle plane between control plane and data plane. The secure middle plane calculates the suspect rate of new flows based on the collected packetsâ information and reports suspect lists to blockchain for immutably storing and sharing. Besides, the smart contract deployed on blockchain in advance constitutes collaborative defense strategies based on the suspect lists reported from multiple SDN domains. When receiving defense strategies, the secure middle plane converts them to specific flow table actions and installs actions into relevant switches. The experimental results indicate that BSD-Guard can efficiently detect DoS/DDoS attacks in multiple controllers scenario and issue precise defensive strategies near the source of attack by identifying the attack path.
As the first blockchain platform supporting smart contracts, Ethereum has become increasingly popular in recent years and generates a massive number of transaction records. Nowadays, millions of accounts in Ethereum have been reported to participate in a variety of businesses, and some of them have been found to be involved in illegal behaviors or even cyber-crimes by exploiting the pseudonymous nature of blockchain. Therefore, there is an urgent need for an effective method to conduct account classification and audit transaction behaviors of each account. In this paper, we model the Ethereum transaction records as a transaction network, and the account classification problem is converted to a node classification problem. Based on the Ethereum transaction network, we propose a novel framework namedFilter andAugmentGraphNeuralNetwork (FA-GNN), which can retain the information of important neighbors and augment node features with high-order information. Experimental results demonstrate that our proposed FA-GNN outperforms state-of-the-art methods in Ethereum account classification.
Path validation assures operational integrity in 5G networks with various network infrastructures where nodes en route are operated by multiple untrusted network slicing authorities. However, in order to correctly validate a path, traditional solutions require the entire path to be revealed to all parties involved, which may potentially expose the network structure to malicious attackers. In this work, we propose a decentralized privacy-preserving path validation protocol utilizing XOR, hashing and Non-interactive zero-knowledge proof (NIZK) that guarantees security and privacy but circumvents performance compromise. We tested our protocols in a simulated multi-authority network to show how the privacy-preserving path validation can protect node privacy without significantly degrading performance.
Abstract Bitcoin is a popular form of cryptocurrencies. Bitcoin provides users' anonymity through cryptographic pseudonyms. Bitcoin operates on a peerâtoâpeer network that maintains a public ledger, called blockchain , to log all transactions from one pseudonym to other, thereby hides the identity of the users. A transaction graph from the blockchain may unveil certain users' identities. To sustain with users' anonymity, mixing is often applied. CoinJoin , MixCoin , CoinShuffle , CoinParty and SecureCoin are some of the Bitcoin protocols that apply mixnet . Mixnet has certain limitations. Firstly, protocols assume âescrow addresseeâ that collects the coins, and performs mixing . The âescrow addresseeâ must be trustworthy. Secondly, protocols which do not assume âescrow addressee,â often use mixnet . Mixnet requires every participant must sign all others transactions. This incurs large volume of multiâsignature. Presently, Bitcoin protocol can include at most 15 multiâsignatures. Therefore, the protocols are not scalable. Finally, mixnet is âallâorânothing.â That is, if all mixnodes are active then only the output is guaranteed. We present a multiparty shuffling protocol to anonymize the Bitcoin transactions. Our protocol is free from âescrow addresseeâ and multiâsignature. The protocol executes in multiple rounds. The protocol is not âallâorânothing.â That means, every round guarantees some degree of anonymity.
Ardeshir Shojaeinasab, Amir Pasha Motamed, Behnam Bahrak
Abstract Cryptocurrencies, particularly Bitcoin, have garnered attention for their potential in anonymous transactions. However, their anonymity has often been compromised by deanonymization attacks. To counter this, mixing services have been introduced. While they enhance privacy, they obscure fund traceability. This study seeks to demystify transactions linked to these services, shedding light on pathways of concealed and laundered money. We propose a method to identify and classify transactions and addresses of major mixing services in Bitcoin. Unlike previous research focusing on older techniques like CoinJoin, we emphasize modern mixing services. We gathered labelled data by transacting with three prominent mixers (MixTum, Blemder, and CryptoMixer) and identified recurring patterns. Using these patterns, an algorithm was created to pinpoint mixing transactions and distinguish mixerârelated addresses. The algorithm achieved a remarkable recall rate of 100%. Given the lack of clear ground truth and the vast number of unlabelled transactions, ensuring accuracy was a challenge. However, by analyzing a set of nonâmixing transactions with our model, it was confirmed that the high recall rate was not misleading. This work provides a significant advancement in monitoring mixing transactions, presenting a valuable tool against fraud and money laundering in cryptocurrency networks.
Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This âall-or-nothingâ authorization mode is not compatible with the âpay-as-you-useâ commercial billing model. In this article, we propose a new notion called time controlled expressive predicate query with accountable anonymity. It realizes time controlled data query, where a time server issues time token to authorize search privilege in designated time period. The data users can anonymously query on encrypted data and the anonymity is accountable in a way that the trusted authority is able to deanonymize data users if they misbehave in the system. The underlying techniques are anonymous credential, Pederson commitment and non-interactive zero-knowledge proof. We firstly design an efficient expressive predicate query (EPQ) scheme, which is proved secure to protect the privacy of expressive search predicate. Based on EPQ, we present a concrete system instantiation, which realizes key-escrow free and time token nontransferability. The formal definition and security models are given out. The system is formally proved indistinguishable against chosen keyword-set attacks, unforgeable of time tokens and accountable of anonymous users. The comparison and experiment results demonstrate its scalability and efficiency.
Gautam Gajra, Omkar Ghadge, Yash Gandhi, Prof. Devita Durge
Abstract: Developing a protected electronic voting system that gives the decency and security of a current voting system, while providing the accuracy and flexibility offered by electronic systems, has been a test for quite a while. Use of blockchain as an assist to actualize disseminated digital balloting structures is accessed. At the present, neither civilians nor elected leaders have favoured conventional ballots. Elections are surrounded via way of means of vote falsification, bribery and different vote casting problems paper and polling shape balloting a ballot system, the checking of vote's takes hours and the number of the time days and hardly ever any activities reduce to rubble the effects through distinctive feature of human or machine. There needs to be an awesome function of current technology to improve the prevailing system. Blunder, which brings about the process taking much more. Blockchain innovation offers a fact in which that character flaw is eliminated from the situation and votes are checked proper away. Blockchain generation is the only one that handles the real vote. We use the stable hash set of rules for resolving this trouble and attempted to convey an answer via using this booming generation. The system based on blockchain will be safe, trustworthy, and private. It will help to realize the general remember of the applicants who participated and its capabilities inside the equal manner as people's religion of their governments does. The citizens and political selection executives can get sizable benefits from the voting poll programming application. And yet, e-vote casting a poll gives large risks to political race safety and honesty and modifications the concept of political selection straightforwardness and investigation. E-Voting has a Relatively bit of freedom; at the cease of the day, Relative Advantage is degree while an improvement is regarded as advanced to the past one; Since, e-Voting is advanced to the guide vote casting system. Keywords: Blockchain, E-voting, Security, Smart Contracts, Immutable
Remote voting has become more critical in recent years, especially since the COVID-19 outbreak. Blockchain technology and its benefits such as decentralization, security, and transparency have given rise to proposals for blockchain-based voting systems. However, the traceability of blockchain transactions violates voter anonymity in existing proposals. Besides, transaction costs also need to be considered. Solutions that may cause repeated elections should be avoided for a low-cost scalable voting system. In this work, we propose ElectAnon, a blockchain-based, self-tallying, and ranked-choice voting protocol focusing on anonymity, robustness, and scalability. ElectAnon achieves anonymity by enabling voters to register with identity commitments and cast their votes via zero-knowledge proofs. Robustness is realized by removing the direct control of the authorities in the voting process by using timed-state machines. Each voter encodes the ballot into a single integer and blinds the vote off-chain while making the verification on-chain. This makes the protocol infinitely scalable in the number of voters. ElectAnon is also a solution for governance in Decentralized Autonomous Organizations (DAO): It includes a candidate proposal module and an algorithm-agnostic mechanism to plug-in different tallying methods easily. The Merkle forest extension is proposed for conducting even more trustless elections. ElectAnon is implemented with smart contracts based on Ethereum Virtual Machine (EVM) and a zero-knowledge gadget, Semaphore. The implementation also includes two different sophisticated tallying methods, Borda Count and Tideman. Experimental results show that a 40-voter and 10-candidate election can be implemented with the gas consumption reduced up to 89% compared to previous works. While other studies could not exceed a 25,000-voter setup, ElectAnon has been observed to run safely for 1,000,000 voters. The implementation can be found at https://github.com/ceyonur/electanon .