Yuri Bespalov, Lyudmila Kovalchuk, Hanna Nelasa, Roman Oliynykov · 5 authors
Sidechains are among the most promising scalability and extended functionality solutions for blockchains. Application of zero knowledge techniques (Latus, Mina) allows for reaching high level security and general throughput, though it brings new challenges on keeping decentralization where significant effort is required for robust computation of zk-proofs. We consider a simultaneous decentralized creation of various zk-proof trees that form proof-trees sequences in sidechains in the model that combines behavior of provers, both deterministic (mutually consistent) or stochastic (independent) and types of proof trees. We define the concept of efficiency of such process, introduce its quantity measure and recommend parameters for tree creation. In deterministic cases, the sequences of published trees are ultimately periodic and ensure the highest possible efficiency (no collisions in proof creation). In stochastic cases, we obtain a universal measure of prover efficiencies given by the explicit formula in one case or calculated by a simulation model in another case. The optimal number of allowed provers’ positions for a step can be set for various sidechain parameters, such as number of provers, number of time steps within one block, etc. Benefits and restrictions for utilization of non-perfect binary proof trees are also explicitly presented.
Justice Odoom, Huang Xiao-fang, Samuel Akwasi Danso, Benedicta Nana Esi Nyarko
Recently, blockchain technology has garnered support. However, an attenuating factor to its global adoption in certain use cases is privacy-preservation owing to its inherent transparency. A widely explored cryptographic option to address this challenge has been ring signature which aside its privacy guarantee must be double spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attack in a Lightweight Ring Signature scheme and proceed to construct a new, fortified commitment scheme using the signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secured and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as Kovan testnet along with performance analysis attesting to efficiency and usability and make the code publicly available on GitHub.
Abstract In view of the node security risks and key management vulnerabilities in heterogeneous sensor networks, a key management protocol for heterogeneous sensor networks based on zero trust security and chaotic neural networks (KMPHSN-ZTSCNN) was proposed. Taking advantages of the decomposition difficulty of singular matrix and chaotic classification characteristics of Hopfield overload chaotic neural network, the node registration and authentication of sensor network were achieved by blockchain and zero-knowledge proof. The channel state information (CSI) and the adjustable mathematical function were relied on to generate a dynamically changing key to complete continuous verification and achieve zero trust security authentications, thus ensuring data security. The protocol can dynamically allocate different keyspace sizes according to the security level of the group, the storage capacity if the nodeand computing capacity and can adapt to the asymmetric structure of heterogeneous sensor networks. Theoretical proof and experimental performance analysis results show that the protocol is feasible and can meet the security requirements of heterogeneous sensor networks.
Within the context of the big data age, data sharing is gradually rising with the embodiment of data value. Data value can increase through sharing, but there are security problems during the period of data sharing such as centralized deployment, malicious theft, and tampering, which greatly affect the security of data. Aiming at the common privacy leakage problem during data sharing, this research builds a data sharing platform on the chain based on blockchain technology, and combines the function encryption technology and zero-knowledge proof technology to realize the sharing of verifiable computing results, and proposes a sharing model. The purpose of this model is to enable the data owner to control the data sharing, so as to guarantee the security and privacy of the data while sharing, ensure that the original data is not leaked, and realize the availability and invisibility of the data. In addition, it is necessary to ensure the reliability of data processing results, eliminate the risk of unreliable data processing caused by original data encryption, and protect the legal right of data users.
Md Ahmad, Gautami Tripathi, Farheen Siddiqui, Mohammad Afshar Alam · 7 authors
The overwhelming popularity of technology-based solutions and innovations to address day-to-day processes has significantly contributed to the emergence of smart cities. where millions of interconnected devices and sensors generate and share huge volumes of data. The easy and high availability of rich personal and public data generated in these digitalized and automated ecosystems renders smart cities vulnerable to intrinsic and extrinsic security breaches. Today, with fast-developing technologies, the classical username and password approaches are no longer adequate to secure valuable data and information from cyberattacks. Multi-factor authentication (MFA) can provide an effective solution to minimize the security challenges associated with legacy single-factor authentication systems (both online and offline). This paper identifies and discusses the role and need of MFA for securing the smart city ecosystem. The paper begins by describing the notion of smart cities and the associated security threats and privacy issues. The paper further provides a detailed description of how MFA can be used for securing various smart city entities and services. A new concept of blockchain-based multi-factor authentication named "BAuth-ZKP" for securing smart city transactions is presented in the paper. The concept focuses on developing smart contracts between the participating entities within the smart city and performing the transactions with zero knowledge proof (ZKP)-based authentication in a secure and privacy-preserved manner. Finally, the future prospects, developments, and scope of using MFA in smart city ecosystem are discussed.
We provide a generic construction to turn any classical Zero-Knowledge (ZK) protocol into a composable (quantum) oblivious transfer (OT) protocol, mostly lifting the round-complexity properties and security guarantees (plain-model/statistical security/unstructured functions...) of the ZK protocol to the resulting OT protocol. Such a construction is unlikely to exist classically as Cryptomania is believed to be different from Minicrypt. In particular, by instantiating our construction using Non-Interactive ZK (NIZK), we provide the first round-optimal (2-message) quantum OT protocol secure in the random oracle model, and round-optimal extensions to string and k-out-of-n OT. At the heart of our construction lies a new method that allows us to prove properties on a received quantum state without revealing additional information on it, even in a non-interactive way, without public-key primitives, and/or with statistical guarantees when using an appropriate classical ZK protocol. We can notably prove that a state has been partially measured (with arbitrary constraints on the set of measured qubits), without revealing any additional information on this set. This notion can be seen as an analog of ZK to quantum states, and we expect it to be of independent interest as it extends complexity theory to quantum languages, as illustrated by the two new complexity classes we introduce, ZKstatesQIP and ZKstatesQMA.
Maryam Sheikhi Garjan, N. Gamze ORHON KILIÇ, Murat Cenk
The increasing demand for secure and anonymous transactions raises the popularity of ring signatures, which is a digital signature scheme that allows identifying a group of possible signers without revealing the identity of the actual signer. This paper presents efficient supersingular isogeny-based ring signature and linkable ring signature schemes that will find potential applications in post-quantum technologies. We develop the ring signature scheme by applying the Fiat-Shamir transform on the sigma protocol for a ring which we obtain from the supersingular isogeny-based interactive zero-knowledge identification scheme by adopting the scheme for a ring. We also extend our ring signature protocol with an additional parameter, i.e., a tag that provides to detect if a signer issues two signatures concerning the same ring by preserving anonymity and linkable anonymity. The signature size of our ring signature protocols increases logarithmically in the size of the ring thanks to the Merkle trees. We show the security proofs and efficiency analyses of the protocols offered. Moreover, we provide the implementation results of the supersingular isogeny-based ring signature, which offers small signature sizes for NIST post-quantum security levels.
We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e.g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i.e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design. We propose a blockchain-based marketplace design to achieve all five objectives mentioned above. In our design, we utilize a distributed storage infrastructure and an aggregator aside from the project owner and the trainers. The aggregator is a processing node that performs certain computations, including assessing trainer contributions, removing outliers, and updating hyper-parameters. We execute the proposed data market through a blockchain smart contract. The deployed smart contract ensures that the project owner cannot evade payment, and honest trainers are rewarded based on their contributions at the end of training. Finally, we implement the building blocks of the proposed data market and demonstrate their applicability in practical scenarios through extensive experiments.
The Internet of Things (IoT) is ubiquitous in our lives. However, the inherent vulnerability of IoT smart devices can lead to the destruction of networks in untrustworthy environments. Therefore, authentication is a necessary tool to ensure the legitimacy of nodes and protect data security. Naturally, the authentication factors always include various sensitive users’ information, such as passwords, ID cards, even biological information, etc. How to prevent privacy leakage has always been a problem faced by the IoT. Zero-knowledge authentication is a crucial cryptographic technology that uses authenticates nodes on the networks without revealing identity or any other data entered by users. However, zero-knowledge proof (ZKP) requires more complex data exchange protocols and more data transmission compared to traditional cryptography technologies. To understand how zero-knowledge authentication works in IoT, we produce a survey on zero-knowledge authentication in privacy-preserving IoT in the paper. First, we overview the IoT architecture and privacy, including security challenges and open question in different IoT layers. Next, we overview zero-knowledge authentication and provide a comprehensive analysis of designing zero-knowledge authentication protocols in various IoT networks. We summarize the advantages of ZKP-based authentication in IoT. Finally, it summarizes the potential problems and future directions of ZKP in IoT.
Abstract As an electronic form of traditional voting, electronic voting is becoming more and more popular in today’s information society. Most of the existing electronic voting protocols need a trusted center to calculate the voting result, but the requirement of a trusted center is often unrealistic and prone to single point of failure. In this regard, the decentralized electronic voting protocols based on blockchain have been proposed. Unfortunately, most existing blockchain-based voting protocols fail to ensure anonymity, legitimacy, and correctness of counting. Besides, they do not satisfy robustness, i.e., the voting result cannot be counted in the event of voter abstention. To address the above challenges, we propose a novel blockchain-based self-tallying voting protocol, where the group signature and zero-knowledge proof are utilized in a way that the voter can securely distribute anonymous and unlinkable electronic ballots, thereby guaranteeing complete anonymity and legitimacy. Meanwhile, a novel signcryption algorithm is designed by combining distributed ElGamal encryption and Paillier encryption algorithms, which enhances the computational efficiency of voting results while supporting robustness. The security proof shows that our protocol ensures the confidentiality of ballots, complete anonymity, legitimacy, fairness, dispute-freeness and resistance against multi-voting. In addition, our protocol satisfies robustness, i.e., voting result can be correctly calculated and verified even if some voters abstain from voting. Finally, extensive experiments show that our protocol greatly reduces the computational cost and communication overhead, and is more practical than existing self-tallying voting protocols.
Vaishnavi Nagaraja, Muhammad Rezal Kamel Ariffin, Terry Shue Chien Lau, Nurul Nur Hanisah Adenan · 7 authors
The identification protocol is a type of zero-knowledge proof. One party (the prover) needs to prove his identity to another party (the verifier) without revealing the secret key to the verifier. One can apply the Fiat–Shamir transformation to convert an identification scheme into a signature scheme which can be used for achieving security purposes and cryptographic purposes, especially for authentication. In this paper, we recall an identification protocol, namely the RankID scheme, and show that the scheme is incorrect and insecure. Then, we proposed a more natural approach to construct the rank version of the AGS identification protocol and show that our construction overcomes the security flaws in the RankID scheme. Our proposal achieves better results when comparing the public key size, secret key size, and signature size with the existing identification schemes, such as Rank RVDC and Rank CVE schemes. Our proposal also achieves 90%, 50%, and 96% reduction for the signature size, secret key size, and public key size when compared to the Rank CVE signature scheme.
Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.
In this paper we consider nonnegatively curved finite dimensional Alexandrov spaces with a non-collapsing condition, i.e., such that unit balls have volumes uniformly bounded from below away from zero. We study the relation between the isoperimetric profile, the existence of isoperimetric sets, and the asymptotic structure at infinity of such spaces. In this setting, we prove that the following conditions are equivalent: the space has linear volume growth; it is Gromov--Hausdorff asymptotic to one cylinder at infinity; it has uniformly bounded isoperimetric profile; the entire space is a tubular neighborhood of either a line or a ray. Moreover, on a space satisfying any of the previous conditions, we prove existence of isoperimetric sets for sufficiently large volumes, and we characterize the geometric rigidity at the level of the isoperimetric profile. Specializing our study to the $2$-dimensional case, we prove that unit balls have always volumes uniformly bounded from below away from zero, and we prove existence of isoperimetric sets for every volume, characterizing also their topology when the space has no boundary. The proofs exploit a variational approach, and in particular apply to Riemannian manifolds with nonnegative sectional curvature and to Euclidean convex bodies. Up to the authors' knowledge, most of the results are new even in these smooth cases.
With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf 1.
Open access
3 source records
cs.CV
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
Guilherme Albuquerque, Carlo Kleber da Silva Rodrigues
Zcash is a proof-of-work (PoW) cryptocurrency that has gained attention due to its promise of enhanced user privacy. Notwithstanding, Zcash’s thorough acceptance notably depends on how profitable its mining process can be. To tackle this issue, we propose an analytical model to compute the mining hashrate under solo mining to achieve a liquid revenue equal to the minimum wage in the United States. In the sequence, we then estimate how profitable Zcash solo mining is based on that computed mining hasrate. Our proposed model spans crucial parameters of the whole mining process. In the experiments, we compare Zcash with the popular Bitcoin and also present a competitive analysis encompassing the ten top cryptocurrencies by market capitalization. Final results highlight that: (i) Zcash owns a value of hmin which is about eight orders of magnitude smaller than that of Bitcoin in all investigated scenarios, which refer to the ten least and most expensive american states in terms of electricity tariff; and (ii) Zcash is the second best cryptocurrency for solo mining among the aforementioned ten cryptocurrencies, being the only one whose protocol implements the concept of zero-knowledge proofs. Within this context, our key contribution is to provide the scientific literature with valuable insights to formally pave the way to develop practical analytical models for PoW-cryptocurrency systems, which may be chiefly valuable regarding competitive analyses in general.
In this paper, we propose a practically efficient model for securely computing rank-based statistics, e.g., median, percentiles and quartiles, over distributed datasets in the malicious setting without leaking individual data privacy. Based on the binary search technique of Aggarwal et al. (EUROCRYPT \textquotesingle 04), we respectively present an interactive protocol and a non-interactive protocol, involving at most $\log ||R||$ rounds, where $||R||$ is the range size of the dataset elements. Besides, we introduce a series of optimisation techniques to reduce the round complexity. Our computing model is modular and can be instantiated with either homomorphic encryption or secret-sharing schemes. Compared to the state-of-the-art solutions, it provides stronger security and privacy while maintaining high efficiency and accuracy. Unlike differential-privacy-based solutions, it does not suffer a trade-off between accuracy and privacy. On the other hand, it only involves $O(N \log ||R||)$ time complexity, which is far more efficient than those bitwise-comparison-based solutions with $O(N^2\log ||R||)$ time complexity, where $N$ is the dataset size. Finally, we provide a UC-secure instantiation with the threshold Paillier cryptosystem and $Σ$-protocol zero-knowledge proofs of knowledge.
In view of the problem that the transaction privacy in the current blockchain technology service is easy to be leaked, a bulletproof alliance chain technology service transaction privacy protection mechanism is proposed. Firstly, this paper uses digital certificates as access mechanisms and stores them on the chain to ensure that the identity of technical service transactions is trusted. Secondly, the transaction data of the technical service user is hidden in the Pedersen commitment, and the Bulletproof is used to build the scope proof. Enable the verifier to conduct confidential verification of the legitimacy of the transaction without obtaining the sensitive information of the transaction, so as to ensure that the user’s transaction privacy is not disclosed. Finally, the security and privacy of the proposed privacy protection scheme are analyzed, and the comparison with other zero-knowledge proof schemes shows that the scheme has the advantages of strong privacy, scalability, and low storage cost.
We demonstrate how to leverage Apple's Find My protocol, most well known as the underlying protocol of the AirTag, for arbitrary data-muling and location services. This provides a new "infrastructure-free" deployment, where areas with frequent human activity can take advantage of this zero-cost backhaul network. While there are severe limitations (e.g. no acknowledgement channel back to the sending device), Find My-based networking could still be a reliable backhaul with sufficient transmission redundancy and knowledge of deployment context. Towards that end, we develop TagAlong, a protocol for scalable, efficient data transmission on the Find My network. We implement a proof-of-concept and demonstrate throughput up to 12.5 bytes/sec and up to a 97% data reception rate.
A central tenet in mechanism design is the ability to irrevocably commit to a mechanism. Commitment is achieved by public declaration, letting players verify incentive properties in advance and the outcome in retrospect. However, public declaration can reveal superfluous information that is private to the mechanism designer, such as her target function or costs. We propose a new approach to commitment, and show how to commit to, and run, any given mechanism without disclosing it, while enabling the verification of incentive properties and the outcome -- all without any mediators. Our framework leverages zero-knowledge proofs -- a cornerstone of modern cryptographic theory.
Giampaolo Bella, Pietro Biondi, Stefano Bognanni, Sergio Esposito
Attackers may attempt exploiting Internet of Things (IoT) devices to operate them unduly as well as to gather personal data of the legitimate device owners’. Vulnerability Assessment and Penetration Testing (VAPT) sessions help to verify the effectiveness of the adopted security measures. However, VAPT over IoT devices, namely VAPT targeted at IoT devices, is an open research challenge due to the variety of target technologies and to the creativity it may require. Therefore, this article aims at guiding penetration testers to conduct VAPT sessions over IoT devices by means of a new cyber Kill Chain (KC) termed PETIoT. Several practical applications of PETIoT confirm that it is general, while its main novelty lies in the combination of attack and defence steps. PETIoT is demonstrated on a relevant example, the best-selling IP camera on Amazon Italy, the TAPO C200 by TP-Link, assuming an attacker who sits on the same network as the device’s in order to assess all the network interfaces of the device. Additional knowledge is generated in terms of three zero-day vulnerabilities found and practically exploited on the camera, one of these with High severity and the other two with Medium severity by the CVSS standard. These are camera Denial of Service (DoS), motion detection breach and video stream breach. The application of PETIoT culminates with the proof-of-concept of a home-made fix, based on an inexpensive Raspberry Pi 4 Model B device, for the last vulnerability. Ultimately, our responsible disclosure with the camera vendor led to the release of a firmware update that fixes all found vulnerabilities, confirming that PetIoT has valid impact in real-world scenarios.
In crowdsourcing systems, requesters publish tasks, and interested workers provide answers to get rewards. Worker anonymity motivates participation since it protects their privacy. Anonymity with unlinkability is an enhanced version of anonymity because it makes it impossible to ``link'' workers across the tasks they participate in. Another core feature of crowdsourcing systems is worker quality which expresses a worker's trustworthiness and quantifies their historical performance. Notably, worker quality depends on the participation history, revealing information about it, while unlinkability aims to disassociate the workers' identities from their past activity. In this work, we present AVeCQ, the first crowdsourcing system that reconciles these properties, achieving enhanced anonymity and verifiable worker quality updates. AVeCQ relies on a suite of cryptographic tools, such as zero-knowledge proofs, to (i) guarantee workers' privacy, (ii) prove the correctness of worker quality scores and task answers, and (iii) commensurate payments. AVeCQ is developed modularly, where the requesters and workers communicate over a platform that supports pseudonymity, information logging, and payments. In order to compare AVeCQ with the state-of-the-art, we prototype it over Ethereum. AVeCQ outperforms the state-of-the-art in three popular crowdsourcing tasks (image annotation, average review, and Gallup polls). For instance, for an Average Review task with $5$ choices and $128$ participating workers AVeCQ is 40\% faster (including overhead to compute and verify the necessary proofs and blockchain transaction processing time) with the task's requester consuming 87\% fewer gas units.
Lennart Linden, Dominik K. Klein, Karl A. Kalina, Jörg Brummund · 6 authors
In the present work, a hyperelastic constitutive model based on neural networks is proposed which fulfills all common constitutive conditions by construction, and in particular, is applicable to compressible material behavior. Using different sets of invariants as inputs, a hyperelastic potential is formulated as a convex neural network, thus fulfilling symmetry of the stress tensor, objectivity, material symmetry, polyconvexity, and thermodynamic consistency. In addition, a physically sensible stress behavior of the model is ensured by using analytical growth terms, as well as normalization terms which ensure the undeformed state to be stress free and with zero energy. In particular, polyconvex, invariant-based stress normalization terms are formulated for both isotropic and transversely isotropic material behavior. By fulfilling all of these conditions in an exact way, the proposed physics-augmented model combines a sound mechanical basis with the extraordinary flexibility that neural networks offer. Thus, it harmonizes the theory of hyperelasticity developed in the last decades with the up-to-date techniques of machine learning. Furthermore, the non-negativity of the hyperelastic neural network-based potentials is numerically examined by sampling the space of admissible deformations states, which, to the best of the authors' knowledge, is the only possibility for the considered nonlinear compressible models. For the isotropic neural network model, the sampling space required for that is reduced by analytical considerations. In addition, a proof for the non-negativity of the compressible Neo-Hooke potential is presented. The applicability of the model is demonstrated by calibrating it on data generated with analytical potentials, which is followed by an application of the model to finite element simulations. In addition, an adaption of the model to noisy data is shown and its [...]
Open access
2 source records
Elasticity and Material Modeling
Model Reduction and Neural Networks
Advanced Numerical Methods in Computational Mathematics
The purpose of blockchain technology is to solve the trust problem between people or institutions and make the communication data and network communication of the Internet. In the past, cryptography lost money. Passwords are used to protect data, and the cost is relatively high. But with blockchain, cryptography becomes valuable. The formation of blockchain has made new contributions to cryptography and done something we could not do in the past. With blockchain, cryptography is "valuable". In fact, there are many cryptographic primitives used in blockchain, such as hash, digital signature, and etc. Moreover, digital signature not only uses standard digital signature, but also uses ring signature, connectable ring signature, one-time signature, borromer ring signature, multi signature, homomorphic encryption, homomorphic commitment, accumulator, zero knowledge proof, etc. As well as the recently popular password signature toss. As mentioned above, the popularity of blockchain technology will completely break the centralized pattern, indicating the advent of a new era in the future - Web3.0. This paper focuses on the application of encryption technology in blockchain and expounds in detail the applications of such as hash function and ring signature in blockchain. This study analyzes the application of cryptography in blockchain and discusses to the development of encryption technology in the future.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques