Biblio

With the rapid development of cloud computing which has been extensively applied in the health research, the concept of medical cloud has become widespread. In this paper, we proposed an integrated medical cloud architecture with multiple applications based on privacy protection. The scheme in this paper adopted attribute encryption to ensure the PHR files encrypted all the time in order to protect the health privacy of the PHR owners not leaked. In addition, the medical cloud architecture proposed in this paper is suitable for multiple application scenarios. Different from the traditional domain division which has public domain (PUD) and private domain (PSD), the PUD domain is further divided into PUD1and PUD2 with finer granularity based on different permissions of the PHR users. In the PUD1, the PHR users have read or write access to the PHR files, while the PHR users in the PUD2 only have read permissions. In the PSD, we use key aggregation encryption (KAE) to realize the access control. For PHR users of PUD1 and PUD2, the outsourcable ABE technology is adopted to greatly reduce the computing burden of users. The results of function and performance test show that the scheme is safe and effective.

With increased awareness comes unprecedented expectations. We live in a digital, cloud era wherein the underlying information architectures are expected to be elastic, secure, resilient, and handle petabyte scaling. The expectation of epic proportions from the next generation of the data frameworks is to not only do all of the above but also build it on a foundation of trust and explainability across multi-organization business networks. From cloud providers to automobile industries or even vaccine manufacturers, components are often sourced by a complex, not full digitized thread of disjoint suppliers. Building Machine Learning and AI-based order fulfillment and predictive models, remediating issues, is a challenge for multi-organization supply chain automation. We posit that Federated Learning in conjunction with blockchain and smart contracts are technologies primed to tackle data privacy and centralization challenges. In this paper, motivated by challenges in the industry, we propose a decentralized distributed system in conjunction with a recommendation system model (Matrix Factorization) that is trained using Federated Learning on an Ethereum blockchain network. We leverage smart contracts that allow decentralized serverless aggregation to update local-ized items vectors. Furthermore, we utilize Homomorphic Encryption (HE) to allow sharing the encrypted gradients over the network while maintaining their privacy. Based on our results, we argue that training a model over a serverless Blockchain network using smart contracts will provide the same accuracy as in a centralized model while maintaining our serverless model privacy and reducing the overhead communication to a central server. Finally, we assert such a system that provides transparency, audit-ready and deep insights into supply chain operations for enterprise cloud customers resulting in cost savings and higher Quality of Service (QoS).

Blockchain is well known for its storage consistency, decentralization and tamper-proof, but the privacy disclosure and difficulty in auditing discourage the innovative application of blockchain technology. As compared to public blockchain and private blockchain, consortium blockchain is widely used across different industries and use cases due to its privacy-preserving ability, auditability and high transaction rate. However, the present co-audit of privacy-preserving data on consortium blockchain is inefficient. Private data is usually encrypted by a session key before being published on a consortium blockchain for privacy preservation. The session key is shared with transaction parties and auditors for their access. For decentralizing auditorial power, multiple auditors on the consortium blockchain jointly undertake the responsibility of auditing. The distribution of the session key to an auditor requires individually encrypting the session key with the public key of the auditor. The transaction initiator needs to be online when each auditor asks for the session key, and one encryption of the session key for each auditor consumes resources. This work proposes GAChain and applies group key agreement technology to efficiently co-audit privacy-preserving data on consortium blockchain. Multiple auditors on the consortium blockchain form a group and utilize the blockchain to generate a shared group encryption key and their respective group decryption keys. The session key is encrypted only once by the group encryption key and stored on the consortium blockchain together with the encrypted private data. Auditors then obtain the encrypted session key from the chain and decrypt it with their respective group decryption key for co-auditing. The group key generation is involved only when the group forms or group membership changes, which happens very infrequently on the consortium blockchain. We implement the prototype of GAChain based on Hyperledger Fabric framework. Our experimental studies demonstrate that GAChain improves the co-audit efficiency of transactions containing private data on Fabric, and its incurred overhead is moderate.

There have been developed image encryption algorithm using chaotic map and DNA pseudo-symbols sequence gained on the basis of real DNA symbols. In the suggested algorithm, the address for the selecting of DNA symbols sequence from Gene Bank, encoding rule of the DNA symbols, also the initial parameters of the chaotic map are determined on the secret key basis. Image pixels modification is based on the numerical values of the chaotic points sets coordinates obtained with the chaos play description of the DNA pseudo-symbols and the transference of pixels is based on displacement table constructed with the chaotic map.

To address the concerns posed by certain security attacks on communication protocol, this paper proposes a Quantum Key Exchange algorithm coupled with an encoding scheme based on Fermat Numbers and DNA sequences. The concept of Watson-Crick’s transformation of DNA sequences and random property of the Fermat Numbers is applied for protection of the communication system by means of dual encryption. The key generation procedure is governed by a quantum bit rotation mechanism. The total process is illustrated with an example. Also, security analysis of the encryption and decryption process is also discussed.

Cloud computing has revolutionized the way how users store, process, and use data. It has evolved over the years to put forward various sophisticated models that offer enhanced performance. The growth of electronic data stored in the Cloud has made it crucial to access data without data loss and leakage. Security threats still prevent significant corporations that use sensitive data to employ cloud computing to handle their data. Traditional cryptographic techniques like DES, AES, etc... provide data confidentiality but are computationally complex. To overcome such complexities, a unique field of cryptography known as DNA Cryptography came into existence. DNA cryptography is a new field of cryptography that utilizes the chemical properties of DNA for secure data encoding. DNA cryptographic algorithms are much faster than traditional cryptographic methods and can bring about greater security with lesser computational costs. In this paper, we have proposed an enhanced DNA cryptosystem involving operations such as encryption, encoding table generation, and decryption based on the chemical properties of DNA. The performance analysis has proven that the proposed DNA cryptosystem is secure and efficient in Cloud data storage.

This paper aims to present a performance comparison of new authenticated encryption (AE) algorithm with the objective of high network security and better efficiency as compared to the defacto standard. This algorithm is based on a critical property of nonce-misuse-resistance incorporating DNA computation for securing the key, here the processing unit of DNA block converts the input key into its equivalent DNA base formats based on the ASCII code table. The need for secure exchange of keys through a public channel has become inevitable and thus, the proposed architecture will enhance the secrecy by using DNA cryptography. These implementations consider Advanced Encryption Standard in Galois Counter mode (AES-GCM) as a standard for comparison.

This paper proposes an image encryption technology based on six-dimensional hyperchaotic system and DNA encoding, in order to solve the problem of low security in existing image encryption algorithms. First of all, the pixel values of the R, G, and B channels are divided into blocks and zero-filled. Secondly, the chaotic sequence generated by the six-dimensional hyperchaotic system and logistic mapping is used for DNA coding and DNA operations. Third, the decoded three-channel pixel values are scrambled through diagonal traversal. Finally, merge the channels to generate a ciphertext image. According to simulation experiments and related performance analysis, the algorithm has high security performance, good encryption and decryption effects, and can effectively resist various common attack methods.

Cryptography is the science of encryption and decryption of data using the techniques of mathematics to achieve secure communication. This enables the user to send the data in an insecure channel. These channels are usually vulnerable to security attacks due to the data that they possess. A lot of work is being done these days to protect data and data communication. Hence securing them is the utmost concern. In recent times a lot of researchers have come up with different cryptographic techniques to protect the data over the network. One such technique used is DNA cryptography. The proposed approach employs a DNA sequencing-based encoding and decoding mechanism. The data is secured over the network using a secure authentication and key agreement procedure. A significant amount of work is done to show how DNA cryptography is secure when compared to other forms of cryptography techniques over the network.

Cloud computing has changed how humans use their technological expertise. It indicates a transition in the use of computers as utilitarian instruments with radical applications in general. However, as technology advances, the number of hazards increases and crucial data protection has become increasingly challenging due to extensive internet use. Every day, new encryption methods are developed, and much research is carried out in the search for a reliable cryptographic algorithm. The AES algorithm employs an overly simplistic algebraic structure. Each block employs the same encryption scheme, and AES is subject to brute force and MITM attacks. AES have not provide d sufficient levels of security; the re is still a need to put further le vels of protection over them. In this regard, DNA cryptography allows you to encrypt a large quantity of data using only a few amount of DNA. This paper combines two methodologies, a DNA-based algorithm and the AES Algorithm, to provide a consi derably more secure data security platform. The DNA cryptography technology and the AES approach are utilized for data encryption and decryption. To improve cloud security, DNA cryptography and AES provide a technologically ideal option.

IoT is still an emerging technology without a lot of standards around it, which makes it difficult to integrate it into existing businesses, what's more, with restricted assets and expanding gadgets that essentially work with touchy information. Thus, information safety has become urgent for coders and clients. Thus, painstakingly chosen and essentially tested encryption calculations should be utilized to grow the gadgets productively, to decrease the danger of leaking the delicate information. This investigation looks at the ECC calculation (Elliptic Curve Cryptography) and Rivest-Shamir-Adleman (RSA) calculation. Furthermore, adding the study of DNA encoding operation in DNA computing with ECC to avoid attackers from getting access to the valuable data.

IT world is migrating towards utilizing cloud computing as an essential data storing and exchanging platform. With the amelioration of technology, a colossal amount of data is generating with time. Cloud computing provides an enormous data storage capacity with the flexibility of accessing it without the time and place restrictions with virtualized resources. Healthcare industries spawn intense amounts of data from various medical instruments and digital records of patients. To access data remotely from any geographical location, the healthcare industry is moving towards cloud computing. EHR and PHR are patient's digital records, which include sensitive information of patients. Apart from all the proficient service provided by cloud computing, security is a primary concern for various organizations. To address the security issue, several cryptographic techniques implemented by researchers worldwide. In this paper, a vigorous cryptographic method discussed which is implemented by combining DNA cryptography and Elliptic Curve Cryptography to protect sensitive data in the cloud.

Homomorphic Encryption (HE) comes as a sophisticated and powerful cryptography system that can preserve the privacy of data in all cases when the data is at rest or even when data is in processing and computing. All the computations needed by the user or the provider can be done on the encrypted data without any need to decrypt it. However, HE has overheads such as big key sizes and long ciphertexts and as a result long execution time. This paper proposes a novel solution for big data analytic based on clustering and the Elliptical Curve Cryptography (ECC). The Extremely Distributed Clustering technique (EDC) has been used to divide big data into several subsets of cloud computing nodes. Different clustering techniques had been investigated, and it was found that using hybrid techniques can improve the performance and efficiency of big data analytic while at the same time data is protected and privacy is preserved using ECC.

In recent times, security and privacy concerns associated with IoT devices have caught the attention of research community. The problem of securing IoT devices is immensely aggravating due to advancement in technology. These IoT devices are resource-constraint i.e. in terms of power, memory, computation, etc., so they are less capable to secure themselves. So we need a better approach to secure IoT devices within the limited resources. Several studies state that for these lightweight IoT devices Elliptic Curve Cryptography (ECC) suits perfectly. But there are several elliptic curve domain parameter standards, which may be used for different security levels. When any ECC based product is deployed then the selection of a suitable elliptic curve standard according to usability is become very important. So we have to choose one suitable standard domain parameter for the required security level. In this paper, two different elliptic curve standard domain parameters named secp256k1 and secp192k1 proposed by an industry consortium named Standards for Efficient Cryptography Group (SECG) [1] are implemented and then analyzed their performances metrics. The performance of each domain parameter is measured in computation time.

In quantum cryptography research area, quantum digital signature is an important research field. To provide a better privacy for users in constructing quantum digital signature, the stronger anonymity of quantum digital signatures is required. Quantum ring signature scheme focuses on anonymity in certain scenarios. Using quantum ring signature scheme, the quantum message signer hides his identity into a group. At the same time, there is no need for any centralized organization when the user uses the quantum ring signature scheme. The group used to hide the signer identity can be immediately selected by the signer himself, and no collaboration between users.Since the quantum finite automaton signature scheme is very efficient quantum digital signature scheme, based on it, we propose a new quantum ring signature scheme. We also showed that the new scheme we proposed is of feasibility, correctness, anonymity, and unforgeability. And furthermore, the new scheme can be implemented only by logical operations, so it is easy to implement.

Recently, federated learning (FL), as an advanced and practical solution, has been applied to deal with privacy-preserving issues in distributed multi-party federated modeling. However, most existing FL methods focus on the same privacy-preserving budget while ignoring various privacy requirements of participants. In this paper, we for the first time propose an algorithm (PLU-FedOA) to optimize the deep neural network of horizontal FL with personalized local differential privacy. For such considerations, we design two approaches: PLU, which allows clients to upload local updates under differential privacy-preserving of personally selected privacy level, and FedOA, which helps the server aggregates local parameters with optimized weight in mixed privacy-preserving scenarios. Moreover, we theoretically analyze the effect on privacy and optimization of our approaches. Finally, we verify PLU-FedOA on real-world datasets.

Keystroke dynamics is a behavioural biometric form of authentication based on the inherent typing behaviour of an individual. While this technique is gaining traction, protecting the privacy of the users is of utmost importance. Fully Homomorphic Encryption is a technique that allows performing computation on encrypted data, which enables processing of sensitive data in an untrusted environment. FHE is also known to be “future-proof” since it is a lattice-based cryptosystem that is regarded as quantum-safe. It has seen significant performance improvements over the years with substantially increased developer-friendly tools. We propose a neural network for keystroke analysis trained using differential privacy to speed up training while preserving privacy and predicting on encrypted data using FHE to keep the users' privacy intact while offering sufficient usability.

Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to its good empirical performance, but sensitive user information can still be inferred from weight updates shared during FL iterations. We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD. The trade-offs between user privacy, global utility, and transmission rate are proved by defining appropriate metrics for FL with LDP. Compared to existing results, the query sensitivity used in LDP is defined as a variable, and a tighter privacy accounting method is applied. The proposed utility bound allows heterogeneous parameters over all users. Our bounds characterize how much utility decreases and transmission rate increases if a stronger privacy regime is targeted. Furthermore, given a target privacy level, our results guarantee a significantly larger utility and a smaller transmission rate as compared to existing privacy accounting methods.

With the development of network, network security has become a topic of increasing concern. Recent years, machine learning technology has become an effective means of network intrusion detection. However, machine learning technology requires a large amount of data for training, and training data often contains privacy information, which brings a great risk of privacy leakage. At present, there are few researches on data privacy protection in the field of intrusion detection. Regarding the issue of privacy and security, we combine differential privacy and machine learning algorithms, including One-class Support Vector Machine (OCSVM) and Local Outlier Factor(LOF), to propose an hybrid intrusion detection system (IDS) with privacy protection. We add Laplacian noise to the original network intrusion detection data set to get differential privacy data sets with different privacy budgets, and proposed a hybrid IDS model based on machine learning to verify their utility. Experiments show that while protecting data privacy, the hybrid IDS can achieve detection accuracy comparable to traditional machine learning algorithms.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

Differential privacy is a concept to quantity the disclosure of private information that is controlled by the privacy parameter ε. However, an intuitive interpretation of ε is needed to explain the privacy loss to data engineers and data subjects. In this paper, we conduct a worst-case study of differential privacy risks. We generalize an existing model and reduce complexity to provide more understandable statements on the privacy loss. To this end, we analyze the impact of parameters and introduce the notion of a global privacy risk and global privacy leak.

Current implementations of Differential Privacy (DP) focus primarily on the privacy of the data release. The planned thesis will investigate steps towards a user-centric approach of DP in the scope of the Internet-of-Things (IoT) which focuses on data subjects, IoT developers, and data analysts. We will conduct user studies to find out more about the often conflicting interests of the involved parties and the encountered challenges. Furthermore, a technical solution will be developed to assist data subjects and analysts in making better informed decisions. As a result, we expect our contributions to be a step towards the development of usable DP for IoT sensor data.

With the rapid development of 5G, the Internet of Things (IoT) and edge computing technologies dramatically improve smart industries' efficiency, such as healthcare, smart agriculture, and smart city. IoT is a data-driven system in which many smart devices generate and collect a massive amount of user privacy data, which may be used to improve users' efficiency. However, these data tend to leak personal privacy when people send it to the Internet. Differential privacy (DP) provides a method for measuring privacy protection and a more flexible privacy protection algorithm. In this paper, we study an estimation problem and propose a new frequency estimation algorithm named MFEA that redesigns the publish process. The algorithm maps a finite data set to an integer range through a hash function, then initializes the data vector according to the mapped value and adds noise through the randomized response. The frequency of all interference data is estimated with maximum likelihood. Compared with the current traditional frequency estimation, our approach achieves better algorithm complexity and error control while satisfying differential privacy protection (LDP).

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between theoretical accuracy of NILM inference and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multishot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.

Noise has been used as a way of protecting privacy of users in public datasets for many decades now. Differential privacy is a new standard to add noise, so that user privacy is protected. When this technique is applied for a single end user data, it's called local differential privacy. In this study, we evaluate the effects of adding noise to generate randomized responses on machine learning models. We generate randomized responses using Gaussian, Laplacian noise on singular end user data as well as correlated end user data. Finally, we provide results that we have observed on a few data sets for various machine learning use cases.