PeerJ Computer Science:Security and Privacyhttps://peerj.com/articles/index.atom?journal=cs&subject=11200Security and Privacy articles published in PeerJ Computer ScienceA Kullback-Liebler divergence-based representation algorithm for malware detectionhttps://peerj.com/articles/cs-14922023-09-222023-09-22Faitouri A. AboaojaAnazida ZainalFuad A. GhalebNorah Saleh AlghamdiFaisal SaeedHusayn Alhuwayji
Background
Malware, malicious software, is the major security concern of the digital realm. Conventional cyber-security solutions are challenged by sophisticated malicious behaviors. Currently, an overlap between malicious and legitimate behaviors causes more difficulties in characterizing those behaviors as malicious or legitimate activities. For instance, evasive malware often mimics legitimate behaviors, and evasion techniques are utilized by legitimate and malicious software.
Problem
Most of the existing solutions use the traditional term of frequency-inverse document frequency (TF-IDF) technique or its concept to represent malware behaviors. However, the traditional TF-IDF and the developed techniques represent the features, especially the shared ones, inaccurately because those techniques calculate a weight for each feature without considering its distribution in each class; instead, the generated weight is generated based on the distribution of the feature among all the documents. Such presumption can reduce the meaning of those features, and when those features are used to classify malware, they lead to a high false alarms.
Method
This study proposes a Kullback-Liebler Divergence-based Term Frequency-Probability Class Distribution (KLD-based TF-PCD) algorithm to represent the extracted features based on the differences between the probability distributions of the terms in malware and benign classes. Unlike the existing solution, the proposed algorithm increases the weights of the important features by using the Kullback-Liebler Divergence tool to measure the differences between their probability distributions in malware and benign classes.
Results
The experimental results show that the proposed KLD-based TF-PCD algorithm achieved an accuracy of 0.972, the false positive rate of 0.037, and the F-measure of 0.978. Such results were significant compared to the related work studies. Thus, the proposed KLD-based TF-PCD algorithm contributes to improving the security of cyberspace.
Conclusion
New meaningful characteristics have been added by the proposed algorithm to promote the learned knowledge of the classifiers, and thus increase their ability to classify malicious behaviors accurately.
Background
Malware, malicious software, is the major security concern of the digital realm. Conventional cyber-security solutions are challenged by sophisticated malicious behaviors. Currently, an overlap between malicious and legitimate behaviors causes more difficulties in characterizing those behaviors as malicious or legitimate activities. For instance, evasive malware often mimics legitimate behaviors, and evasion techniques are utilized by legitimate and malicious software.
Problem
Most of the existing solutions use the traditional term of frequency-inverse document frequency (TF-IDF) technique or its concept to represent malware behaviors. However, the traditional TF-IDF and the developed techniques represent the features, especially the shared ones, inaccurately because those techniques calculate a weight for each feature without considering its distribution in each class; instead, the generated weight is generated based on the distribution of the feature among all the documents. Such presumption can reduce the meaning of those features, and when those features are used to classify malware, they lead to a high false alarms.
Method
This study proposes a Kullback-Liebler Divergence-based Term Frequency-Probability Class Distribution (KLD-based TF-PCD) algorithm to represent the extracted features based on the differences between the probability distributions of the terms in malware and benign classes. Unlike the existing solution, the proposed algorithm increases the weights of the important features by using the Kullback-Liebler Divergence tool to measure the differences between their probability distributions in malware and benign classes.
Results
The experimental results show that the proposed KLD-based TF-PCD algorithm achieved an accuracy of 0.972, the false positive rate of 0.037, and the F-measure of 0.978. Such results were significant compared to the related work studies. Thus, the proposed KLD-based TF-PCD algorithm contributes to improving the security of cyberspace.
Conclusion
New meaningful characteristics have been added by the proposed algorithm to promote the learned knowledge of the classifiers, and thus increase their ability to classify malicious behaviors accurately.Hybrid post-quantum Transport Layer Security formal analysis in Maude-NPA and its parallel versionhttps://peerj.com/articles/cs-15562023-09-222023-09-22Duong Dinh TranCanh Minh DoSantiago EscobarKazuhiro Ogata
This article presents a security formal analysis of the hybrid post-quantum Transport Layer Security (TLS) protocol, a quantum-resistant version of the TLS protocol proposed by Amazon Web Services as a precaution in dealing with future attacks from quantum computers. In addition to a classical key exchange algorithm, the proposed protocol uses a post-quantum key encapsulation mechanism, which is believed invulnerable under quantum computers, so the protocol’s key negotiation is called the hybrid key exchange scheme. One of our assumptions about the intruder’s capabilities is that the intruder is able to break the security of the classical key exchange algorithm by utilizing the power of large quantum computers. For the formal analysis, we use Maude-NPA and a parallel version of Maude-NPA (called Par-Maude-NPA) to conduct experiments. The security properties under analysis are (1) the secrecy property of the shared secret key established between two honest principals with the classical key exchange algorithm, (2) a similar secrecy property but with the post-quantum key encapsulation mechanism, and (3) the authentication property. Given the time limit T = 1,722 h (72 days), Par-Maude-NPA found a counterexample of (1) at depth 12 in T, while Maude-NPA did not find it in T. At the same time T, Par-Maude-NPA did not find any counterexamples of (2) and (3) up to depths 12 and 18, respectively, and neither did Maude-NPA. Therefore, the protocol does not enjoy (1), while it enjoys (2) and (3) up to depths 12 and 18, respectively. Subsequently, the secrecy property of the master secret holds for the protocol up to depth 12.
This article presents a security formal analysis of the hybrid post-quantum Transport Layer Security (TLS) protocol, a quantum-resistant version of the TLS protocol proposed by Amazon Web Services as a precaution in dealing with future attacks from quantum computers. In addition to a classical key exchange algorithm, the proposed protocol uses a post-quantum key encapsulation mechanism, which is believed invulnerable under quantum computers, so the protocol’s key negotiation is called the hybrid key exchange scheme. One of our assumptions about the intruder’s capabilities is that the intruder is able to break the security of the classical key exchange algorithm by utilizing the power of large quantum computers. For the formal analysis, we use Maude-NPA and a parallel version of Maude-NPA (called Par-Maude-NPA) to conduct experiments. The security properties under analysis are (1) the secrecy property of the shared secret key established between two honest principals with the classical key exchange algorithm, (2) a similar secrecy property but with the post-quantum key encapsulation mechanism, and (3) the authentication property. Given the time limit T = 1,722 h (72 days), Par-Maude-NPA found a counterexample of (1) at depth 12 in T, while Maude-NPA did not find it in T. At the same time T, Par-Maude-NPA did not find any counterexamples of (2) and (3) up to depths 12 and 18, respectively, and neither did Maude-NPA. Therefore, the protocol does not enjoy (1), while it enjoys (2) and (3) up to depths 12 and 18, respectively. Subsequently, the secrecy property of the master secret holds for the protocol up to depth 12.A lightweight intrusion detection method for IoT based on deep learning and dynamic quantizationhttps://peerj.com/articles/cs-15692023-09-222023-09-22Zhendong WangHui ChenShuxin YangXiao LuoDahai LiJunling Wang
Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.
Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.Modelling and verification of post-quantum key encapsulation mechanisms using Maudehttps://peerj.com/articles/cs-15472023-09-192023-09-19Víctor GarcíaSantiago EscobarKazuhiro OgataSedat AkleylekAyoub Otmani
Communication and information technologies shape the world’s systems of today, and those systems shape our society. The security of those systems relies on mathematical problems that are hard to solve for classical computers, that is, the available current computers. Recent advances in quantum computing threaten the security of our systems and the communications we use. In order to face this threat, multiple solutions and protocols have been proposed in the Post-Quantum Cryptography project carried on by the National Institute of Standards and Technologies. The presented work focuses on defining a formal framework in Maude for the security analysis of different post-quantum key encapsulation mechanisms under assumptions given under the Dolev-Yao model. Through the use of our framework, we construct a symbolic model to represent the behaviour of each of the participants of the protocol in a network. We then conduct reachability analysis and find a man-in-the-middle attack in each of them and a design vulnerability in Bit Flipping Key Encapsulation. For both cases, we provide some insights on possible solutions. Then, we use the Maude Linear Temporal Logic model checker to extend the analysis of the symbolic system regarding security, liveness and fairness properties. Liveness and fairness properties hold while the security property does not due to the man-in-the-middle attack and the design vulnerability in Bit Flipping Key Encapsulation.
Communication and information technologies shape the world’s systems of today, and those systems shape our society. The security of those systems relies on mathematical problems that are hard to solve for classical computers, that is, the available current computers. Recent advances in quantum computing threaten the security of our systems and the communications we use. In order to face this threat, multiple solutions and protocols have been proposed in the Post-Quantum Cryptography project carried on by the National Institute of Standards and Technologies. The presented work focuses on defining a formal framework in Maude for the security analysis of different post-quantum key encapsulation mechanisms under assumptions given under the Dolev-Yao model. Through the use of our framework, we construct a symbolic model to represent the behaviour of each of the participants of the protocol in a network. We then conduct reachability analysis and find a man-in-the-middle attack in each of them and a design vulnerability in Bit Flipping Key Encapsulation. For both cases, we provide some insights on possible solutions. Then, we use the Maude Linear Temporal Logic model checker to extend the analysis of the symbolic system regarding security, liveness and fairness properties. Liveness and fairness properties hold while the security property does not due to the man-in-the-middle attack and the design vulnerability in Bit Flipping Key Encapsulation.A new hybrid method combining search and direct based construction ideas to generate all 4 × 4 involutory maximum distance separable (MDS) matrices over binary field extensionshttps://peerj.com/articles/cs-15772023-09-192023-09-19Gökhan TuncayFatma Büyüksaraçoğlu SakallıMeltem Kurt PehlivanoğluGülsüm Gözde YılmazgüçSedat AkleylekMuharrem Tolga Sakallı
This article presents a new hybrid method (combining search based methods and direct construction methods) to generate all
$4 \times 4$4×4
involutory maximum distance separable (MDS) matrices over
$\mathbf{F}_{2^m}$F2m
. The proposed method reduces the search space complexity at the level of
$$\sqrt n $$
n, where n represents the number of all
$4 \times 4$4×4
invertible matrices over
$\mathbf{F}_{2^m}$F2m
to be searched for. Hence, this enables us to generate all
$4 \times 4$4×4
involutory MDS matrices over
$\mathbf{F}_{2^3}$F23
and
$\mathbf{F}_{2^4}$F24
. After applying global optimization technique that supports higher Exclusive-OR (XOR) gates (e.g., XOR3, XOR4) to the generated matrices, to the best of our knowledge, we generate the lightest involutory/non-involutory MDS matrices known over
$\mathbf{F}_{2^3}$F23
,
$\mathbf{F}_{2^4}$F24
and
$\mathbf{F}_{2^8}$F28
in terms of XOR count. In this context, we present new
$4 \times 4$4×4
involutory MDS matrices over
$\mathbf{F}_{2^3}$F23
,
$\mathbf{F}_{2^4}$F24
and
$\mathbf{F}_{2^8}$F28
, which can be implemented by 13 XOR operations with depth 5, 25 XOR operations with depth 5 and 42 XOR operations with depth 4, respectively. Finally, we denote a new property of Hadamard matrix, i.e., (involutory and MDS) Hadamard matrix form is, in fact, a representative matrix form that can be used to generate a small subset of all
$2^k\times 2^k$2k×2k
involutory MDS matrices, where k > 1. For k = 1, Hadamard matrix form can be used to generate all involutory MDS matrices.
This article presents a new hybrid method (combining search based methods and direct construction methods) to generate all
$4 \times 4$4×4
involutory maximum distance separable (MDS) matrices over
$\mathbf{F}_{2^m}$F2m
. The proposed method reduces the search space complexity at the level of
$$\sqrt n $$
n, where n represents the number of all
$4 \times 4$4×4
invertible matrices over
$\mathbf{F}_{2^m}$F2m
to be searched for. Hence, this enables us to generate all
$4 \times 4$4×4
involutory MDS matrices over
$\mathbf{F}_{2^3}$F23
and
$\mathbf{F}_{2^4}$F24
. After applying global optimization technique that supports higher Exclusive-OR (XOR) gates (e.g., XOR3, XOR4) to the generated matrices, to the best of our knowledge, we generate the lightest involutory/non-involutory MDS matrices known over
$\mathbf{F}_{2^3}$F23
,
$\mathbf{F}_{2^4}$F24
and
$\mathbf{F}_{2^8}$F28
in terms of XOR count. In this context, we present new
$4 \times 4$4×4
involutory MDS matrices over
$\mathbf{F}_{2^3}$F23
,
$\mathbf{F}_{2^4}$F24
and
$\mathbf{F}_{2^8}$F28
, which can be implemented by 13 XOR operations with depth 5, 25 XOR operations with depth 5 and 42 XOR operations with depth 4, respectively. Finally, we denote a new property of Hadamard matrix, i.e., (involutory and MDS) Hadamard matrix form is, in fact, a representative matrix form that can be used to generate a small subset of all
$2^k\times 2^k$2k×2k
involutory MDS matrices, where k > 1. For k = 1, Hadamard matrix form can be used to generate all involutory MDS matrices.DAFuzz: data-aware fuzzing of in-memory data storeshttps://peerj.com/articles/cs-15922023-09-192023-09-19Yingpei ZengFengming ZhuSiyi ZhangYu YangSiyu YiYufan PanGuojie XieTing Wu
Fuzzing has become an important method for finding vulnerabilities in software. For fuzzing programs expecting structural inputs, syntactic- and semantic-aware fuzzing approaches have been particularly proposed. However, they still cannot fuzz in-memory data stores sufficiently, since some code paths are only executed when the required data are available. In this article, we propose a data-aware fuzzing method, DAFuzz, which is designed by considering the data used during fuzzing. Specifically, to ensure different data-sensitive code paths are exercised, DAFuzz first loads different kinds of data into the stores before feeding fuzzing inputs. Then, when generating inputs, DAFuzz ensures the generated inputs are not only syntactically and semantically valid but also use the data correctly. We implement a prototype of DAFuzz based on Superion and use it to fuzz Redis and Memcached. Experiments show that DAFuzz covers 13~95% more edges than AFL, Superion, AFL++, and AFLNet, and discovers vulnerabilities over 2.7× faster. In total, we discovered four new vulnerabilities in Redis and Memcached. All the vulnerabilities were reported to developers and have been acknowledged and fixed.
Fuzzing has become an important method for finding vulnerabilities in software. For fuzzing programs expecting structural inputs, syntactic- and semantic-aware fuzzing approaches have been particularly proposed. However, they still cannot fuzz in-memory data stores sufficiently, since some code paths are only executed when the required data are available. In this article, we propose a data-aware fuzzing method, DAFuzz, which is designed by considering the data used during fuzzing. Specifically, to ensure different data-sensitive code paths are exercised, DAFuzz first loads different kinds of data into the stores before feeding fuzzing inputs. Then, when generating inputs, DAFuzz ensures the generated inputs are not only syntactically and semantically valid but also use the data correctly. We implement a prototype of DAFuzz based on Superion and use it to fuzz Redis and Memcached. Experiments show that DAFuzz covers 13~95% more edges than AFL, Superion, AFL++, and AFLNet, and discovers vulnerabilities over 2.7× faster. In total, we discovered four new vulnerabilities in Redis and Memcached. All the vulnerabilities were reported to developers and have been acknowledged and fixed.Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systemshttps://peerj.com/articles/cs-15522023-09-042023-09-04Qaiser AbbasSadaf HinaHamza SajjadKhurram Shabih ZaidiRehan Akbar
Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.
Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.A wormhole attack detection method for tactical wireless sensor networkshttps://peerj.com/articles/cs-14492023-08-292023-08-29Ke Zhang
Wireless sensor networks (WSNs) are networks formed by organizing and combining tens of thousands of sensor nodes freely through wireless communication technology. WSNs are commonly affected by various attacks, such as identity theft, black holes, wormholes, protocol spoofing, etc. As one of the more severe threats, wormholes create passive attacks that are hard to detect and eliminate. Since WSN is often used in the tactical network field, a planned secure network is essential for military applications with high security. Guard nodes are traffic monitoring nodes used to supervise neighbors’ data communication around the tactical networks. Therefore, this work proposes a Quality of Service (QoS) security mechanism to select multiple dual-layer guard nodes at different paths of the WSN based on the path qualities to detect wormholes. The entire network’s links are categorized into high, normal, and low priority levels. As such, this study aimed to confirm the security of high priority nodes and links in the tactical network, avoid excessive overhead, and provide random security facilities to all nodes. The proposed measures of the QoS-based security provision, including link cluster formation, guard node selection, authenticated guard node identification, and intrusion detection, ensure economic and efficient network communication with different quality levels.
Wireless sensor networks (WSNs) are networks formed by organizing and combining tens of thousands of sensor nodes freely through wireless communication technology. WSNs are commonly affected by various attacks, such as identity theft, black holes, wormholes, protocol spoofing, etc. As one of the more severe threats, wormholes create passive attacks that are hard to detect and eliminate. Since WSN is often used in the tactical network field, a planned secure network is essential for military applications with high security. Guard nodes are traffic monitoring nodes used to supervise neighbors’ data communication around the tactical networks. Therefore, this work proposes a Quality of Service (QoS) security mechanism to select multiple dual-layer guard nodes at different paths of the WSN based on the path qualities to detect wormholes. The entire network’s links are categorized into high, normal, and low priority levels. As such, this study aimed to confirm the security of high priority nodes and links in the tactical network, avoid excessive overhead, and provide random security facilities to all nodes. The proposed measures of the QoS-based security provision, including link cluster formation, guard node selection, authenticated guard node identification, and intrusion detection, ensure economic and efficient network communication with different quality levels.A message recovery attack on multivariate polynomial trapdoor functionhttps://peerj.com/articles/cs-15212023-08-282023-08-28Rashid AliMuhammad Mubashar HussainShamsa KanwalFahima HajjejSaba Inam
Cybersecurity guarantees the exchange of information through a public channel in a secure way. That is the data must be protected from unauthorized parties and transmitted to the intended parties with confidentiality and integrity. In this work, we mount an attack on a cryptosystem based on multivariate polynomial trapdoor function over the field of rational numbers Q. The developers claim that the security of their proposed scheme depends on the fact that a polynomial system consisting of 2n (where n is a natural number) equations and 3n unknowns constructed by using quasigroup string transformations, has infinitely many solutions and finding exact solution is not possible. We explain that the proposed trapdoor function is vulnerable to a Gröbner basis attack. Selected polynomials in the corresponding Gröbner basis can be used to recover the plaintext against a given ciphertext without the knowledge of the secret key.
Cybersecurity guarantees the exchange of information through a public channel in a secure way. That is the data must be protected from unauthorized parties and transmitted to the intended parties with confidentiality and integrity. In this work, we mount an attack on a cryptosystem based on multivariate polynomial trapdoor function over the field of rational numbers Q. The developers claim that the security of their proposed scheme depends on the fact that a polynomial system consisting of 2n (where n is a natural number) equations and 3n unknowns constructed by using quasigroup string transformations, has infinitely many solutions and finding exact solution is not possible. We explain that the proposed trapdoor function is vulnerable to a Gröbner basis attack. Selected polynomials in the corresponding Gröbner basis can be used to recover the plaintext against a given ciphertext without the knowledge of the secret key.A novel privacy protection method of residents’ travel trajectories based on federated blockchain and InterPlanetary file systems in smart citieshttps://peerj.com/articles/cs-14952023-07-272023-07-27Fenghan LiuPan Wang
The government does have to record and analyze the travel trajectories of urban residents aiming to effectively control the epidemic during COVID-19. However, these privacy-related data are usually stored in centralized cloud databases, which are prone to be vulnerable to cyber attacks leading to personal trajectory information leakage. In this article, we proposed a novel secure sharing and storing method of personal travel trajectory data based on BC and InterPlanetary File System (IPFS). We adopt the Hyperledger Fabric, the representative of Federated BC framework, combined with the IPFS storage to form a novel mode of querying on-chain and storing off-chain aiming to both achieve the effectiveness of data processing and protect personal privacy-related information. This method firstly solves the efficiency problem of traditional public BC and ensures the security of stored data by storing the ciphertext of complete personal travel trajectory data in decentralized IPFS storage. Secondly, considering the huge amount of information of residents’ travel trajectories, the method proposed in this article can obtain the complete information under the chain stored in IPFS by querying the index on the chain, which significantly improves the data processing efficiency of residents’ travel trajectories and thus promotes the effective control of the new crown pneumonia epidemic. Finally, the feasibility of the proposed solution is verified through performance evaluation and security analysis.
The government does have to record and analyze the travel trajectories of urban residents aiming to effectively control the epidemic during COVID-19. However, these privacy-related data are usually stored in centralized cloud databases, which are prone to be vulnerable to cyber attacks leading to personal trajectory information leakage. In this article, we proposed a novel secure sharing and storing method of personal travel trajectory data based on BC and InterPlanetary File System (IPFS). We adopt the Hyperledger Fabric, the representative of Federated BC framework, combined with the IPFS storage to form a novel mode of querying on-chain and storing off-chain aiming to both achieve the effectiveness of data processing and protect personal privacy-related information. This method firstly solves the efficiency problem of traditional public BC and ensures the security of stored data by storing the ciphertext of complete personal travel trajectory data in decentralized IPFS storage. Secondly, considering the huge amount of information of residents’ travel trajectories, the method proposed in this article can obtain the complete information under the chain stored in IPFS by querying the index on the chain, which significantly improves the data processing efficiency of residents’ travel trajectories and thus promotes the effective control of the new crown pneumonia epidemic. Finally, the feasibility of the proposed solution is verified through performance evaluation and security analysis.