Visible to the public Biblio

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2021-05-25
Bakhtiyor, Abdurakhimov, Zarif, Khudoykulov, Orif, Allanov, Ilkhom, Boykuziev.  2020.  Algebraic Cryptanalysis of O'zDSt 1105:2009 Encryption Algorithm. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—7.
In this paper, we examine algebraic attacks on the O'zDSt 1105:2009. We begin with a brief review of the meaning of algebraic cryptanalysis, followed by an algebraic cryptanalysis of O'zDSt 1105:2009. Primarily O'zDSt 1105:2009 encryption algorithm is decomposed and each transformation in it is algebraic described separately. Then input and output of each transformation are expressed with other transformation, encryption key, plaintext and cipher text. Created equations, unknowns on it and degree of unknowns are analyzed, and then overall result is given. Based on experimental results, it is impossible to save all system of equations that describes all transformations in O'zDSt 1105:2009 standard. Because, this task requires 273 bytes for the second round. For this reason, it is advisable to evaluate the parameters of the system of algebraic equations, representing the O'zDSt 1105:2009 standard, theoretically.
Tashev, Komil, Rustamova, Sanobar.  2020.  Analysis of Subject Recognition Algorithms based on Neural Networks. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—4.
This article describes the principles of construction, training and use of neural networks. The features of the neural network approach are indicated, as well as the range of tasks for which it is most preferable. Algorithms of functioning, software implementation and results of work of an artificial neural network are presented.
Karimov, Madjit, Tashev, Komil, Rustamova, Sanobar.  2020.  Application of the Aho-Corasick algorithm to create a network intrusion detection system. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—5.
One of the main goals of studying pattern matching techniques is their significant role in real-world applications, such as the intrusion detection systems branch. The purpose of the network attack detection systems NIDS is to protect the infocommunication network from unauthorized access. This article provides an analysis of the exact match and fuzzy matching methods, and discusses a new implementation of the classic Aho-Korasik pattern matching algorithm at the hardware level. The proposed approach to the implementation of the Aho-Korasik algorithm can make it possible to ensure the efficient use of resources, such as memory and energy.
Ajorlou, Amir, Abbasfar, Aliazam.  2020.  An Optimized Structure of State Channel Network to Improve Scalability of Blockchain Algorithms. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :73—76.
Nowadays, blockchain is very common and widely used in various fields. The properties of blockchain-based algorithms such as being decentralized and uncontrolled by institutions and governments, are the main reasons that has attracted many applications. The security and the scalability limitations are the main challenges for the development of these systems. Using second layer network is one of the various methods proposed to improve the scalability of these systems. This network can increase the total number of transactions per second by creating extra channels between the nodes that operate in a different layer not obligated to be on consensus ledger. In this paper, the optimal structure for the second layer network has been presented. In the proposed structure we try to distribute the parameters of the second layer network as symmetrically as possible. To prove the optimality of this structure we first introduce the maximum scalability bound, and then calculate it for the proposed structure. This paper will show how the second layer method can improve the scalability without any information about the rate of transactions between nodes.
Satılmış, Hami, Akleylek, Sedat.  2020.  Efficient Implementation of HashSieve Algorithm for Lattice-Based Cryptography. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :75—79.
The security of lattice-based cryptosystems that are secure for the post-quantum period is based on the difficulty of the shortest vector problem (SVP) and the closest vector problem (CVP). In the literature, many sieving algorithms are proposed to solve these hard problems. In this paper, efficient implementation of HashSieve sieving algorithm is discussed. A modular software library to have an efficient implementation of HashSieve algorithm is developed. Modular software library is used as an infrastructure in order for the HashSieve efficient implementation to be better than the sample in the literature (Laarhoven's standard HashSieve implementation). According to the experimental results, it is observed that HashSieve efficient implementation has a better running time than the example in the literature. It is concluded that both implementations are close to each other in terms of the memory space used.
ÇELİK, Mahmut, ALKAN, Mustafa, ALKAN, Abdulkerim Oğuzhan.  2020.  Protection of Personal Data Transmitted via Web Service Against Software Developers. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :88—92.
Through the widespread use of information technologies, institutions have started to offer most of their services electronically. The best example of this is e-government. Since institutions provide their services to the electronic environment, the quality of the services they provide increases and their access to services becomes easier. Since personal information can be verified with inter-agency information sharing systems, wrong or unfair transactions can be prevented. Since information sharing between institutions is generally done through web services, protection of personal data transmitted via web services is of great importance. There are comprehensive national and international regulations on the protection of personal data. According to these regulations, protection of personal data shared between institutions is a legal obligation; protection of personal data is an issue that needs to be handled comprehensively. This study, protection of personal data shared between institutions through web services against software developers is discussed. With a proposed application, it is aimed to take a new security measure for the protection of personal data. The proposed application consists of a web interface prepared using React and Java programming languages and rest services that provide anonymization of personal data.
[Anonymous].  2020.  B-DCT based Watermarking Algorithm for Patient Data Protection in IoMT. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :1—4.
Internet of Medical Things (IoMT) is the connection between medical devices and information systems to share, collect, process, store, and integrate patient and health data using network technologies. X-Rays, MR, MRI, and CT scans are the most frequently used patient medical image data. These images usually include patient information in one of the corners of the image. In this research work, to protect patient information, a new robust and secure watermarking algorithm developed for a selected region of interest (ROI) of medical images. First ROI selected from the medical image, then selected part divided equal blocks and applied Discrete Cosine Transformation (DCT) algorithm to embed a watermark into the selected coefficients. Several geometric and removal attacks are applied to the watermarked multimedia element such as lossy image compression, the addition of Gaussian noise, denoising, filtering, median filtering, sharpening, contrast enhancement, JPEG compression, and rotation. Experimental results show very promising results in PSNR and similarity ratio (SR) values after blocked DCT (B-DCT) based embedding algorithm against the Discrete Wavelet Transformation (DWT), Least Significant Bits (LSB) and DCT algorithms.
Susilo, Willy, Duong, Dung Hoang, Le, Huy Quoc.  2020.  Efficient Post-quantum Identity-based Encryption with Equality Test. 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS). :633—640.
Public key encryption with equality test (PKEET) enables the testing whether two ciphertexts encrypt the same message. Identity-based encryption with equality test (IBEET) simplify the certificate management of PKEET, which leads to many potential applications such as in smart city applications or Wireless Body Area Networks. Lee et al. (ePrint 2016) proposed a generic construction of IBEET scheme in the standard model utilising a 3-level hierachy IBE together with a one-time signature scheme, which can be instantiated in lattice setting. Duong et al. (ProvSec 2019) proposed the first direct construction of IBEET in standard model from lattices. However, their scheme achieve CPA security only. In this paper, we improve the Duong et al.'s construction by proposing an IBEET in standard model which achieves CCA2 security and with smaller ciphertext and public key size.
Ahmedova, Oydin, Mardiyev, Ulugbek, Tursunov, Otabek.  2020.  Generation and Distribution Secret Encryption Keys with Parameter. 2020 International Conference on Information Science and Communications Technologies (ICISCT). :1—4.
This article describes a new way to generate and distribute secret encryption keys, in which the processes of generating a public key and formicating a secret encryption key are performed in algebra with a parameter, the secrecy of which provides increased durability of the key.
AKCENGİZ, Ziya, Aslan, Melis, Karabayır, Özgür, Doğanaksoy, Ali, Uğuz, Muhiddin, Sulak, Fatih.  2020.  Statistical Randomness Tests of Long Sequences by Dynamic Partitioning. 2020 International Conference on Information Security and Cryptology (ISCTURKEY). :68—74.
Random numbers have a wide usage in the area of cryptography. In practice, pseudo random number generators are used in place of true random number generators, as regeneration of them may be required. Therefore because of generation methods of pseudo random number sequences, statistical randomness tests have a vital importance. In this paper, a randomness test suite is specified for long binary sequences. In literature, there are many randomness tests and test suites. However, in most of them, to apply randomness test, long sequences are partitioned into a certain fixed length and the collection of short sequences obtained is evaluated instead. In this paper, instead of partitioning a long sequence into fixed length subsequences, a concept of dynamic partitioning is introduced in accordance with the random variable in consideration. Then statistical methods are applied. The suggested suite, containing four statistical tests: Collision Tests, Weight Test, Linear Complexity Test and Index Coincidence Test, all of them work with the idea of dynamic partitioning. Besides the adaptation of this approach to randomness tests, the index coincidence test is another contribution of this work. The distribution function and the application of all tests are given in the paper.
Diao, Yiqing, Ye, Ayong, Cheng, Baorong, Zhang, Jiaomei, Zhang, Qiang.  2020.  A Dummy-Based Privacy Protection Scheme for Location-Based Services under Spatiotemporal Correlation. 2020 International Conference on Networking and Network Applications (NaNA). :443—447.
The dummy-based method has been commonly used to protect the users location privacy in location-based services, since it can provide precise results and generally do not rely on a third party or key sharing. However, the close spatiotemporal correlation between the consecutively reported locations enables the adversary to identify some dummies, which lead to the existing dummy-based schemes fail to protect the users location privacy completely. To address this limit, this paper proposes a new algorithm to produce dummy location by generating dummy trajectory, which naturally takes into account of the spatiotemporal correlation all round. Firstly, the historical trajectories similar to the user's travel route are chosen as the dummy trajectories which depend on the distance between two trajectories with the help of home gateway. Then, the dummy is generated from the dummy trajectory by taking into account of time reachability, historical query similarity and the computation of in-degree/out-degree. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the users real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the users location privacy effectively and efficiently.
2021-03-22
Ban, T. Q., Nguyen, T. T. T., Long, V. T., Dung, P. D., Tung, B. T..  2020.  A Benchmarking of the Effectiveness of Modular Exponentiation Algorithms using the library GMP in C language. 2020 International Conference on Computational Intelligence (ICCI). :237–241.
This research aims to implement different modular exponentiation algorithms and evaluate the average complexity and compare it to the theoretical value. We use the library GMP to implement seven modular exponentiation algorithms. They are Left-to-right Square and Multiply, Right-to-left Square and Multiply, Left-to-right Signed Digit Square, and Multiply Left-to-right Square and Multiply Always Right-to-left Square and Multiply Always, Montgomery Ladder and Joye Ladder. For some exponent bit length, we choose 1024 bits and execute each algorithm on many exponent values and count the average numbers of squares and the average number of multiplications. Whenever relevant, our programs will check the consistency relations between the registers at the end of the exponentiation.
2020-07-06
Chegenizadeh, Mostafa, Ali, Mohammad, Mohajeri, Javad, Aref, Mohammad Reza.  2019.  An Anonymous Attribute-based Access Control System Supporting Access Structure Update. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :85–91.
It is quite common nowadays for clients to outsource their personal data to a cloud service provider. However, it causes some new challenges in the area of data confidentiality and access control. Attribute-based encryption is a promising solution for providing confidentiality and fine-grained access control in a cloud-based cryptographic system. Moreover, in some cases, to preserve the privacy of clients and data, applying hidden access structures is required. Also, a data owner should be able to update his defined access structure at any time when he is online or not. As in several real-world application scenarios like e-health systems, the anonymity of recipients, and the possibility of updating access structures are two necessary requirements. In this paper, for the first time, we propose an attribute-based access control scheme with hidden access structures enabling the cloud to update access structures on expiry dates defined by a data owner.
Saffar, Zahra, Mohammadi, Siamak.  2019.  Fault tolerant non-linear techniques for scalar multiplication in ECC. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :104–113.
Elliptic curve cryptography (ECC) has shorter key length than other asymmetric cryptography algorithms such as RSA with the same security level. Existing faults in cryptographic computations can cause faulty results. If a fault occurs during encryption, false information will be sent to the destination, in which case channel error detection codes are unable to detect the fault. In this paper, we consider the error detection in elliptic curve scalar multiplication point, which is the most important operation in ECC. Our technique is based on non-linear error detection codes. We consider an algorithm for scalar multiplication point proposed by Microsoft research group. The proposed technique in our methods has less overhead for additions (36.36%) and multiplications (34.84%) in total, compared to previous works. Also, the proposed method can detect almost 100% of injected faults.
Nejatifar, Abbas, Hadavi, Mohammad Ali.  2019.  Threat Extraction in IoT-Based Systems Focusing on Smart Cities. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :92–98.
IoT-based services are widely increasing due to their advantages such as economy, automation, and comfort. Smart cities are among major applications of IoT-based systems. However, security and privacy threats are vital issues challenging the utilization of such services. Connectivity nature, variety of data technology, and volume of data maintained through these systems make their security analysis a difficult process. Threat modeling is one the best practices for security analysis, especially for complex systems. This paper proposes a threat extraction method for IoT-based systems. We elaborate on a smart city scenario with three services including lighting, car parking, and waste management. Investigating on these services, firstly, we identify thirty-two distinct threat types. Secondly, we distinguish threat root causes by associating a threat to constituent parts of the IoT-based system. In this way, threat instances can be extracted using the proposed derivation rules. Finally, we evaluate our method on a smart car parking scenario as well as on an E-Health system and identify more than 50 threat instances in each cases to show that the method can be easily generalized for other IoT-based systems whose constituent parts are known.
Attarian, Reyhane, Hashemi, Sattar.  2019.  Investigating the Streaming Algorithms Usage in Website Fingerprinting Attack Against Tor Privacy Enhancing Technology. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :33–38.
Website fingerprinting attack is a kind of traffic analysis attack that aims to identify the URL of visited websites using the Tor browser. Previous website fingerprinting attacks were based on batch learning methods which assumed that the traffic traces of each website are independent and generated from the stationary probability distribution. But, in realistic scenarios, the websites' concepts can change over time (dynamic websites) that is known as concept drift. To deal with data whose distribution change over time, the classifier model must update its model permanently and be adaptive to concept drift. Streaming algorithms are dynamic models that have these features and lead us to make a comparison of various representative data stream classification algorithms for website fingerprinting. Given to our experiments and results, by considering streaming algorithms along with statistical flow-based network traffic features, the accuracy grows significantly.
Balouchestani, Arian, Mahdavi, Mojtaba, Hallaj, Yeganeh, Javdani, Delaram.  2019.  SANUB: A new method for Sharing and Analyzing News Using Blockchain. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :139–143.
Millions of news are being exchanged daily among people. With the appearance of the Internet, the way of broadcasting news has changed and become faster, however it caused many problems. For instance, the increase in the speed of broadcasting news leads to an increase in the speed of fake news creation. Fake news can have a huge impression on societies. Additionally, the existence of a central entity, such as news agencies, could lead to fraud in the news broadcasting process, e.g. generating fake news and publishing them for their benefits. Since Blockchain technology provides a reliable decentralized network, it can be used to publish news. In addition, Blockchain with the help of decentralized applications and smart contracts can provide a platform in which fake news can be detected through public participation. In this paper, we proposed a new method for sharing and analyzing news to detect fake news using Blockchain, called SANUB. SANUB provides features such as publishing news anonymously, news evaluation, reporter validation, fake news detection and proof of news ownership. The results of our analysis show that SANUB outperformed the existing methods.
Farhadi, Majid, Bypour, Hamideh, Mortazavi, Reza.  2019.  An efficient secret sharing-based storage system for cloud-based IoTs. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :122–127.
Internet of Things is the newfound information architecture based on the Internet that develops interactions between objects and services in a secure and reliable environment. As the availability of many smart devices rises, secure and scalable mass storage systems for aggregate data is required in IoTs applications. In this paper, we propose a new method for storing aggregate data in IoTs by use of ( t, n) -threshold secret sharing scheme in the cloud storage. In this method, original data is divided into t blocks that each block is considered as a share. This method is scalable and traceable, i.e., new data can be inserted or part of original data can be deleted, without changing shares, also cloud service providers' fault in sending invalid shares are detectable.
Epishkina, Anna, Finoshin, Mikhail, Kogos, Konstantin, Yazykova, Aleksandra.  2019.  Timing Covert Channels Detection Cases via Machine Learning. 2019 European Intelligence and Security Informatics Conference (EISIC). :139–139.
Currently, packet data networks are widespread. Their architectural features allow constructing covert channels that are able to transmit covert data under the conditions of using standard protection measures. However, encryption or packets length normalization, leave the possibility for an intruder to transfer covert data via timing covert channels (TCCs). In turn, inter-packet delay (IPD) normalization leads to reducing communication channel capacity. Detection is an alternative countermeasure. At the present time, detection methods based on machine learning are widely studied. The complexity of TCCs detection based on machine learning depends on the availability of traffic samples, and on the possibility of an intruder to change covert channels parameters. In the current work, we explore the cases of TCCs detection via
2020-03-23
Noorbehbahani, Fakhroddin, Rasouli, Farzaneh, Saberi, Mohammad.  2019.  Analysis of Machine Learning Techniques for Ransomware Detection. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :128–133.

In parallel with the increasing growth of the Internet and computer networks, the number of malwares has been increasing every day. Today, one of the newest attacks and the biggest threats in cybersecurity is ransomware. The effectiveness of applying machine learning techniques for malware detection has been explored in much scientific research, however, there is few studies focused on machine learning-based ransomware detection. In this paper, the effectiveness of ransomware detection using machine learning methods applied to CICAndMal2017 dataset is examined in two experiments. First, the classifiers are trained on a single dataset containing different types of ransomware. Second, different classifiers are trained on datasets of 10 ransomware families distinctly. Our findings imply that in both experiments random forest outperforms other tested classifiers and the performance of the classifiers are not changed significantly when they are trained on each family distinctly. Therefore, the random forest classification method is very effective in ransomware detection.

Bahrani, Ala, Bidgly, Amir Jalaly.  2019.  Ransomware detection using process mining and classification algorithms. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :73–77.

The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.

2020-03-09
El Balmany, Chawki, Asimi, Ahmed, Tbatou, Zakariae, Asimi, Younes, Guezzaz, Azidine.  2019.  Openstack: Launch a Secure User Virtual Machine Image into a Trust Public Cloud IaaS Environment. 2019 4th World Conference on Complex Systems (WCCS). :1–6.

Cloud Management Platforms (CMP) have been developed in recent years to set up cloud computing architecture. Infrastructure-as-a-Service (IaaS) is a cloud-delivered model designed by the provider to gather a set of IT resources which are furnished as services for user Virtual Machine Image (VMI) provisioning and management. Openstack is one of the most useful CMP which has been developed for industry and academic researches to simulate IaaS classical processes such as launch and store user VMI instance. In this paper, the main purpose is to adopt a security policy for a secure launch user VMI across a trust cloud environment founded on a combination of enhanced TPM remote attestation and cryptographic techniques to ensure confidentiality and integrity of user VMI requirements.

2019-08-26
Barthe, Gilles, Fan, Xiong, Gancher, Joshua, Grégoire, Benjamin, Jacomme, Charlie, Shi, Elaine.  2018.  Symbolic Proofs for Lattice-Based Cryptography. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :538–555.

Symbolic methods have been used extensively for proving security of cryptographic protocols in the Dolev-Yao model, and more recently for proving security of cryptographic primitives and constructions in the computational model. However, existing methods for proving security of cryptographic constructions in the computational model often require significant expertise and interaction, or are fairly limited in scope and expressivity. This paper introduces a symbolic approach for proving security of cryptographic constructions based on the Learning With Errors assumption (Regev, STOC 2005). Such constructions are instances of lattice-based cryptography and are extremely important due to their potential role in post-quantum cryptography. Following (Barthe, Grégoire and Schmidt, CCS 2015), our approach combines a computational logic and deducibility problems—a standard tool for representing the adversary's knowledge, the Dolev-Yao model. The computational logic is used to capture (indistinguishability-based) security notions and drive the security proofs whereas deducibility problems are used as side-conditions to control that rules of the logic are applied correctly. We then use AutoLWE, an implementation of the logic, to deliver very short or even automatic proofs of several emblematic constructions, including CPA-PKE (Gentry et al., STOC 2008), (Hierarchical) Identity-Based Encryption (Agrawal et al. Eurocrypt 2010), Inner Product Encryption (Agrawal et al. Asiacrypt 2011), CCA-PKE (Micciancio et al., Eurocrypt 2012). The main technical novelty beyond AutoLWE is a set of (semi-)decision procedures for deducibility problems, using extensions of Gröbner basis computations for subalgebras in the (non-)commutative setting (instead of ideals in the commutative setting). Our procedures cover the theory of matrices, which is required for lattice-based assumption, as well as the theory of non-commutative rings, fields, and Diffie-Hellman exponentiation, in its standard, bilinear and multilinear forms. Additionally, AutoLWE supports oracle-relative assumptions, which are used specifically to apply (advanced forms of) the Leftover Hash Lemma, an information-theoretical tool widely used in lattice-based proofs.

Paletov, Rumen, Tsankov, Petar, Raychev, Veselin, Vechev, Martin.  2018.  Inferring Crypto API Rules from Code Changes. Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation. :450–464.
Creating and maintaining an up-to-date set of security rules that match misuses of crypto APIs is challenging, as crypto APIs constantly evolve over time with new cryptographic primitives and settings, making existing ones obsolete. To address this challenge, we present a new approach to extract security fixes from thousands of code changes. Our approach consists of: (i) identifying code changes, which often capture security fixes, (ii) an abstraction that filters irrelevant code changes (such as refactorings), and (iii) a clustering analysis that reveals commonalities between semantic code changes and helps in eliciting security rules. We applied our approach to the Java Crypto API and showed that it is effective: (i) our abstraction effectively filters non-semantic code changes (over 99% of all changes) without removing security fixes, and (ii) over 80% of the code changes are security fixes identifying security rules. Based on our results, we identified 13 rules, including new ones not supported by existing security checkers.
Chiu, Pei-Ling, Lee, Kai-Hui.  2018.  Optimization Based Adaptive Tagged Visual Cryptography. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :33–34.
The Tagged Visual Cryptography Scheme (TVCS)1 adds tag images to the noise-like shares generated by the traditional VCS to improve the shares management of the traditional VCS. However, the existing TVCSs suffers visual quality of the recovered secret image may be degraded and there may be pixel expansion. This study proposes a Threshold Adaptive Tagged Visual Cryptography Scheme ((k, n)-ATVCS) to solve the above-mentioned problems. The ATVCS encryption problem is formulated in a mathematical optimization model, and an evolutionary algorithm is developed to find the optimal solution to the problem. The proposed (k, n)-ATVCS enables the encryptor to adjust the visual quality between the tag image and the secret image by tuning parameters. Experimental results show the correctness and effectiveness of this study.