Paillier Research Articles

A homomorphic approach for security and privacy preservation of Smart Airports

The concept of Smart Airport is emerging in the Aviation sector because of the industry 4.0 revolution. To provide the best facility to the air travelers, most of the airports have enhanced their infrastructure using smart automation and Cloud Services (CS), etc. which play a significant role in increasing the revenue of the aviation industry. As the use of digital technology at airports is increasing, they are becoming progressively vulnerable to cyber-attacks and data breaches. The data collected by the airports is stored in specialized cloud databases, which ensures the availability of the data at any time. However, cloud servers have been compromised several times which leads to a lack of trust by the consumers. To meet the privacy and security objectives of the data collected at the airports and stored in the cloud, this research proposes a Searchable Encryption (SE) scheme based on Partial Homomorphic Encryption (PHE). The proposed scheme allows users to search on the encrypted data stored in the cloud hence enhancing the usability of the encrypted data. In this paper, a Single Keyword Searchable Encryption (SKSE) scheme is presented which is implemented using Paillier Cryptosystem. The proposed scheme comprises of two variants (a) Secure SKSE (b) Efficient SKSE. The Secure SKSE scheme exploits the probabilistic properties of the Paillier cryptosystem, whereas, the Efficient SKSE scheme is lightweight as it is based on the deterministic properties of the Paillier cryptosystem. Both variants of SKSE scheme are deployed on a public cloud platform “Contabo” and tested on the real-world aviation data to assess the performance of both approaches. The security and performance analysis illustrates that the proposed Secure SKSE maintains a high-security level by providing probabilistic encryption and trapdoors. On the other hand, the Efficient SKSE is 84 times faster in terms of performance.

A secure double spectrum auction scheme

With the arrival of the 5G era, wireless communication technologies and services are rapidly exhausting the limited spectrum resources. Spectrum auctions came into being, which can effectively utilize spectrum resources. Because of the complexity of the electronic spectrum auction network environment, the security of spectrum auction can not be guaranteed. Most scholars focus on researching the security of the single-sided auctions, while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and buyers. Researchers begin to design the secure double spectrum auction mechanisms, in which two semi-honest agents are introduced to finish the spectrum auction rules. But these two agents may collude with each other or be bribed by buyers and sellers, which may create security risks, therefore, a secure double spectrum auction is proposed in this paper. Unlike traditional secure double spectrum auctions, the spectrum auction server with Software Guard Extensions (SGX) component is used in this paper, which is an Ethereum blockchain platform that performs spectrum auctions. A secure double spectrum protocol is also designed, using SGX technology and cryptographic tools such as Paillier cryptosystem, stealth address technology and one-time ring signatures to well protect the private information of spectrum auctions. In addition, the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification, and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum auctions. Finally, security analysis and performance evaluation of our protocol are discussed.

BSIF: Blockchain-Based Secure, Interactive, and Fair Mobile Crowdsensing

Given the explosive growth of portable devices, mobile crowdsensing (MCS) is becoming an essential approach that fully utilizes pervasive idle resources to accomplish sensing tasks. The traditional MCS relies on the centralized server for task handle is susceptible to a single point of failure. Targeting this security issue, researchers have proposed a series of blockchain-based MCS. However, nodes in the blockchain suffer from high computation cost for data processing. Simultaneously, most blockchain-based MCS systems lack an efficient incentive mechanism for service requesters and workers. In this work, we integrate the smart contract and mobile devices to establish a secure, interactive, and fair blockchain-based MCS system called BSIF. To prevent illegitimate participants, BSIF requests all users to verify their identities using private keys from the registration phase. In the case of worker location privacy leakage, the location-based symmetric key generator is adopted to coordinate a session key for target range worker selection. Besides, we transfer the data evaluation process to the requester side (e.g., a personal computer), reducing computation cost in the blockchain nodes. Due to the homomorphic feature of the Paillier Cryptosystem and common interest, the requester cannot violate the directives from the blockchain. Subsequently, the Stackelberg game is adopted to investigate the participation level of the workers and the fair reward mechanism for the requesters to achieve a dynamic balance. Finally, the security analysis and performance evaluation demonstrate that our BSIF can defend against possible adversaries while significantly cutting overhead and giving participants the utmost incentive.

Securely Computing the Manhattan Distance under the Malicious Model and Its Applications

Manhattan distance is mainly used to calculate the total absolute wheelbase of two points in the standard coordinate system. The secure computation of Manhattan distance is a new geometric problem of secure multi-party computation. At present, the existing research secure computing protocols for Manhattan distance cannot resist the attack of malicious participants. In the real scene, the existence of malicious participants makes it necessary to study a solution that can resist malicious attacks. This paper first analyzes malicious attacks of the semi-honest model protocol of computing Manhattan distance and then designs an advanced protocol under the malicious model by using the Goldwasser–Micali encryption system and Paillier encryption algorithm, and utilizing some cryptographic tools such as the cut-choose method and zero-knowledge proof. Finally, the real/ideal model paradigm method is used to prove the security of the malicious model protocol. Compared with existing protocols, the experimental simulation shows that the proposed protocol can resist malicious participant attacks while maintaining high efficiency. It has practical value.

Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified.

Privacy-Preserving and Low-Latency Federated Learning in Edge Computing

Edge computing has been widely used in recent years for bringing services closer to end users, resulting in faster response for applications. However, the sensitive information that leaves the data owner is at risk of being disclosed because the service provider is generally honest-but-curious. Federated learning (FL) is a popular method for preserving privacy by transferring the model from the edge node to local devices and training on the local data set. Nonetheless, the training parameter that communicates between local mobile devices and the edge node may contain the original data and be guessed by adversaries. In order to address the privacy threats, we propose the PL-FedIPEC scheme in this article, which is a privacy-preserving and low-latency FL method that transmits parameters encrypted with the improved Paillier, a homomorphic encryption algorithm, to protect the privacy of end devices without transmitting data to the edge node. Our method introduces the improved Paillier encryption, which brings a new hyperparameter and previously computes multiple random intermediate values in the key generation phase so that the time for the encryption phase has a significant reduction. With this new algorithm, the time for model training is decreased, and the sensitive information is in ciphertext format and cannot be analyzed. To evaluate the efficiency of our proposed scheme, we conduct extensive experiments and the results validate and demonstrate that our scheme with the improved Paillier algorithm can achieve the same accuracy as the original Paillier algorithm and the baseline FedAVG algorithm. At the same time, our method can save a massive amount of time when training the learning model with various settings.

Privacy-enhancing data aggregation and data analytics in wireless networks for a large class of distributed queries

Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir’s Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS.

Secure Judgment of Point and Line Relationship Against Malicious Adversaries and Its Applications

<p>With the rapid development of the Internet and information technology, the problem of zero-trust networks has become increasingly prominent, and secure multi-party computation has become a research hotspot to solve the problem of zero-trust networks. The secure judgment of point and line relationship is an important research branch of secure computing set geometry. However, most of resent secure computing protocols of point and line relationship are designed in the semi-honest model and cannot resist malicious attacks. Therefore, this paper analyzes the possible malicious adversary behaviors and designs a secure protocol in the malicious model. In this paper, the Paillier cryptosystem, zero- knowledge proof, and cut-choose method are used to resist malicious behavior, and the real/ideal model paradigm method is used to prove the security of the protocol. Compared with the existing solutions, the malicious model protocol is still efficient and widely used in real applications.</p> <p> </p>

Horizontally Partitioned Data Publication with Differential Privacy

In this paper, we study the privacy-preserving data publishing problem in a distributed environment. The data contain sensitive information; hence, directly pooling and publishing the local data will lead to privacy leaks. To solve this problem, we propose a multiparty horizontally partitioned data publishing method under differential privacy (HPDP-DP). First, in order to make the noise level of the published data in the distributed scenario the same as in the centralized scenario, we use the infinite divisibility of the Laplace distribution to design a distributed noise addition scheme to perturb the locally shared data and use Paillier encryption to transmit the locally shared data to the semitrusted curator. Then, the semitrusted curator obtains the estimator of the covariance matrix of the aggregated data with Laplace noise and then obtains the principal components of the aggregated data and returns them to each data owner. Finally, the data owner utilizes the generative model of probabilistic principal component analysis to generate a synthetic data set for publication. We conducted experiments on different real data sets; the experimental results demonstrate that the synthetic data set released by the HPDP-DP method can maintain high utility.

Secure Covid-19 Electronic Health Records Management for Telediagnosis and Travel Ticket Assistant System Using Cryptographic Approaches.

Presently, the whole world is suffering from the Covid-19 pandemic. In this harmful situation, using information and Internet technology is mandatory for the government and medical practitioners. After the lockdown, the government needs to take important decisions to allow passengers to travel through air, rail, and land. In the present situation, people need to get a medical report from the hospitals to travel through various modes of transport. In this regard, the Covid-19 history of the passengers plays an important role in issuing tickets to the passengers. Hence, in this paper, a novel authentication method using InterPlanetary File System (IPFS) is suggested to retrieve the Covid-19 history of all passengers to determine whether to issue tickets and allow people to travel through various modes of transport. The government can share the Covid-19 status of passengers with the ticket issuing authority. The medical practitioners can share medical reports and medical images of such people for telediagnosis. To provide security, a novel privacy-preserving storage and sharing of Covid-19 records using secure authentication and image cryptosystem are proposed using chaos, cryptographic hash (SHA-256), Paillier cryptosystem, and IPFS. Security analysis shows that the system can withstand various kinds of attacks.

Privacy-preserving decentralized price coordination for EV charging stations

Price competition among electric vehicle (EV) charging stations is as fierce as the competition among gas stations. Nash equilibrium (NE) is a solution concept that can characterize a competition’s efficient and stable state. However, calculation of the equilibrium is often time-consuming and requires complete information on the charging stations. Rapidly changing charging stations often hinder reaching equilibrium. In this study, we analyze price competition with service capacity constraints and use an ordinal potential game framework to investigate the structure of the competition. By constructing the ordinal potential function, the equilibrium characterization is converted to identifying the solution through a single-objective optimization. We further propose a decentralized algorithm to enable effective price coordination to achieve equilibrium with maximized social welfare. To preserve the privacy of charging stations from internal collusion and external attacks, an advanced secure multi-party computation technology known as the Paillier Cryptosystem is customized for our proposed decentralized algorithm. Numerical studies based on field data suggest the significance of our framework.

Efficient Privacy-Preserving Federated Learning With Unreliable Users

Federated learning (FL) has emerged as a powerful technology widely applied in Internet of Things (IoT). Recently, researchers have shown an increased interest in privacy-preserving FL with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . The goal of such works is to achieve private training under ciphertext mode while ensuring that the FL model is mainly derived from the contributions of users with high-quality data. However, the existing work is still in its infancy, and the main challenge faced by many researchers is how to achieve their schemes for meeting the demands of high accuracy and efficiency. To combat that, we propose an efficient privacy-preserving FL (EPPFL) scheme with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> . Specifically, we design a novel scheme to mitigate the negative impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unreliable users</i> , where the targeted model is guaranteed to be updated with high-quality data. Through iteratively executing our “Excluding Irrelevant Components” and “Weighted Aggregation,” the FL model converges rapidly while taking limited communication and computation overhead. As a result, not only the model accuracy can be optimized, but also the training efficiency can be improved. Meanwhile, we conduct a secure framework based on the threshold Paillier cryptosystem, which can rigorously protect all user-related private information during the training process. Furthermore, the extensive experiments demonstrate our EPPFL with high-level performance in terms of accuracy and efficiency.

Event-triggered privacy-preserving bipartite consensus for multi-agent systems based on encryption

In this paper, the privacy-preserving-based bipartite consensus control problem is investigated for a class of discrete-time nonlinear multi-agent systems (MASs). The classical bipartite consensus control implementation relies on the explicit information exchange between agents, which will lead to the possible leakage of agents’ private information, especially the initial states. Therefore, to prevent data disclosure to neighbors inside the MASs and the outside eavesdroppers, the data is encrypted by means of Paillier encryption scheme during information transmission. This means each agent first sends the relative data in the form of ciphertext to its neighbors for further encryption process, and then restores the control protocol based on the returned encrypted information from the neighbors. Since homomorphic encryption only works for integers, the data must be quantized before encryption, and it will inevitably result in the quantization error. On the other hand, for the purpose of saving communication resources, both decentralized and distributed event-triggered schemes are also proposed. Resorting to the Lyapunov stability theory and algebraic graph theory, sufficient conditions are derived ensuring that the MASs subject to a confidential communication agreement can achieve the bounded bipartite consensus. Two numerical examples are presented to verify the validity of the theoretical results.

PPDMIT: a lightweight architecture for privacy-preserving data aggregation in the Internet of Things

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preservation. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been causing a big challenge in this field. To improve the efficiency of this trade-off rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages Homomorphic Paillier Encryption (HPE), K-means, a One-way hash chain, and the Chinese Remainder Theorem (CRT). We have found that the proposed privacy-preserving architecture achieves more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted an 8.096% improvement over LPDA and 6.508% over PPIOT.

Paillier Cryptosystem Based ChainNode for Secure Electronic Voting

Blockchain is a distributed and decentralized ledger of transactions that are linked together cryptographically leading to immutability and tamper-resistance, thereby ensuring the integrity of data. Due to the ability of blockchain to guarantee the integrity of data, it has found wide-range adoption in electronic voting (e-voting) systems in recent years, this is in a bid to prevent manipulation of votes. However, due to the distributed nature of the blockchain, opportunities arise for privacy intrusion of the data being secured. The translation of this privacy flaw in blockchain to e-voting systems is the possibility of violation of the privacy of the electorates. Consequently, in a bid to achieve integrity and privacy of votes in e-voting, this study presents the use of an open-source blockchain system, coupled with a privacy-oriented cryptosystem known as the Paillier cryptosystem, towards addressing the privacy concerns of the blockchain. The performance of the system was evaluated and a transaction throughput of 1424 tps was obtained for ten thousand simulated ballot transactions. Further evaluation was carried out on the system, by increasing the number of system transactions. This showed that the mining time of the blockchain increased by an average factor of 0.18 s for every thousand increases in the number of transactions. Also, the response time of the system to a range of user actions was evaluated over an increasing number of voters. Results obtained showed that the response time of the system for vote casting operations increased by an average of 0.33 min per thousand voters while for vote tallying there was an increase in response time by an average of 0.848 min per thousand voters. The scientific value of this study is the development of an integrity and privacy-preserving e-voting system consisting of an open-source nodechain coupled with a privacy-oriented cryptosystem known as the Paillier cryptosystem following the security requirements of e-voting systems. The proposed system addresses the issue of integrity in e-voting while still maintaining the privacy of the electorates.

Implementasi MD5 dan Paillier Cryptosystem Untuk Membuat Tanda Tangan Digital

Of the several benefits of cryptographic techniques is data authentication. Before the digital era, signatures were used as a means to prove the authenticity of printed documents such as letters, certificates, certificates, books and so on. Documents that do not have a signature will be doubtful for their authenticity. One of the characteristics of these signatures is that all the signature patterns are the same, so this situation can easily be manipulated with signature forgery, if the related parties use a different signature on each document that is signed it will also have an impact on changing doubts on the parties who are signed. reserves the right to verify. To overcome the need, documents should be given a digital signature. The intended digital signature is not a manual signature that is scanned and then embedded in a digital document. Digital signatures will be built from cryptographic techniques. In this study, the signature that will be used is a signature with a combination of a hash function algorithm and a web-based public-key cryptography. The methods used are the MD5 algorithm and Paillier Cryptosystem. Paillier cryposystem is a public-key cryptographic algorithm which is believed to be difficult to attack because it uses n-residue class calculations so that it will be very difficult to find the key pair used, this is known as the Composite Residuosity (CR) assumption.

Privacy-Preserving KNN Classification Algorithm for Smart Grid

With the development of Internet of Things (IOT), outsourcing data and tasks to a cloud server has become a popular and economical way for small devices with restricted ability. The k-nearest neighbors (KNNs) classification algorithm have been commonly applied to medical image classification, abnormal detection, defective product identification, and so on. The previous privacy-preserving KNN algorithms are based on two cloud servers, which have high computational and communication costs. In this paper, we design a privacy-preserving KNN classification (PPKC) algorithm with single cloud server for smart grid. Specifically, each smart meter and control center encrypted their data with Paillier cryptosystem and the cloud server does some calculation on the encrypted data. We prove that PPKC can protect the privacy of both smart meters and control centers, and the classification results are also private for the server. Besides, both smart meters and control centers can stay offline after uploading their data. The experiment results demonstrate that the proposed PPKC algorithm is more efficient than the previous algorithms and it can obtain almost the same accuracy as the original KNN, which means the PPKC algorithm is more applicable for small devices in IOT.

EPMDA-FED: Efficient and Privacy-Preserving Multidimensional Data Aggregation Scheme With Fast Error Detection in Smart Grid

Smart grids bring advantages of reliabilty and high efficiency by real-time communication technologies in contrast to the traditional grids. However, smart grids also raise concerns about privacy and security for the individual fine-grained information collection. In order to guarantee privacy and securtiy in the grids, we propose an efficient and privacy-preserving multidimensional data aggregation scheme without a third trusted party and supporting fast error detection, named EPMDA-FED, in the paper. Firstly, we adopt Chinese Remainder Theorem to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center. Secondly, our scheme is efficient for encryption without high-cost additive homomorphic public key encryption scheme, such as Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computation complexity. Besides, through security analysis, EPMDA-FED is semantically secure against collusion attack and the consistency of negotiated key, authenticity and data integrity of the users’ reports are guaranteed. Finally, performance evaluation shows EPMDA-FED is more efficient than existing competing approaches in terms of computation and communication overhead.

Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing

In mobile crowdsensing, truth discovery (TD) enables a crowdsensing server to extract truthful information from possibly conflicting crowdsensing data. TD provides a more accurate truth estimation than traditional truth inference methods like majority voting and averaging. However, there still exist crucial data privacy (including sensory data, inferred truths, and intermediates) and practicability (e.g., efficiency, utility, and non-interaction) concerns in real-world crowdsensing applications. Existing researches either fail to provide adequate data privacy protection throughout the entire TD procedure or suffer from low practicability. In this paper, we propose two schemes: a basic privacy-aware TD scheme (BPTD) and a privacy-enhanced TD scheme (PETD) with two servers for mobile crowdsensing, comprehensively considering both privacy and practicability. BPTD is straightforwardly conducted on shared data with few user-side interactions, while achieving high efficiency. To further liberate mobile users and prevent disclosure of the intermediates, PETD incorporates a novel partial decryption-based Paillier Cryptosystem to work with secret sharing, offering enhanced privacy protection without relying on any user-side involvement. Additionally, we improve the efficiency of PETD via data packing. Security analysis shows the desired privacy goals. Compared to prior studies with the best security guarantees, our extensive experiments demonstrate a comparable and superior performance regarding different metrics.

A privacy preserving data aggregation and query for metro passenger flow via mobile crowdsensing

SummaryWith the popularity of mobile Internet networks and mobile terminal equipment, mobile crowdsensing (MCS) has become a popular and economical way of data collection, sharing, and query services. However, the existing privacy‐preserving query schemes MCS‐based less consider the sensing user's privacy leakage at data aggregation phase and querier's privacy leakage at query service phase simultaneously. In this article, we propose a novel privacy‐preserving aggregation and query scheme applied in metro passenger flow via MCS, which achieves the metro passenger flow acquisition and query service and protects the sensing user's identity and location privacy as well as the querier's query privacy. We exploit Paillier cryptosystem and pseudonym mechanism to prevent the privacy leakage, and utilize secure kNN algorithm to protect the query privacy. Meanwhile, we achieve the sensing user accountability based on digital signature algorithm and credible management. The privacy analysis demonstrates that our scheme satisfies the privacy‐preserving and security requirements. The performance evaluation and experimental result show the efficiency of our scheme in practice.