Abstract

As the world grapples with the COVID-19 and its variants, multi-user collaboration by means of cloud computing is ubiquitous. How to make better use of cloud resources while preventing user privacy leakage has become particularly important. Multi-key homomorphic encryption(MKHE) can effectively deal with the privacy disclosure issue during the multi-user collaboration in the cloud computing setting. Firstly, we improve the DGHV homomorphic scheme by modifying the selection of key and the coefficients in encryption, so as to eliminate the restriction on the parity of the ciphertext modulus in the public key. On this basis, we further propose a DGHV-type MKHE scheme based on the number theory. In our scheme, an extended key is introduced for ciphertext extension, and we prove that it is efficient in performance analysis. The semantic security of our schemes is proved under the assumption of error-free approximate greatest common divisor and the difficulty of large integer factorization. Furthermore, the simulation experiments show the availability and computational efficiency of our MKHE scheme. Therefore, our scheme is suitable for the multi-user scenario in cloud environment.

Introduction

In the post-pandemic era, telecommuting has become an indispensable working mode, and emerging technologies such as cloud computing have played an important role in epidemic monitoring, prevention and control and medical assistance, showing broader application prospects and growth potential [1], [2]. According to Gartner, from 2015 to 2020, the penetration rate of global cloud computing market represented by IaaS, PaaS and SaaS increased year by year from 4.3% to 13.1%, and will rise to 15.3% in the next year. The market size is 208.3 billion dollars in 2020 and will exceed 600 billion dollars in 2025 [3].

Cloud services can eliminate the storage space constraints of personal devices and reduce local computing overhead. In practice, different companies or organizations store data in the cloud and use the cloud to share data with other members. Designing a one-to-many data sharing scheme based on attribute-based encryption(ABE) [4], [5] can achieve effective access control, but ABE is not suitable for collaborative computing between multiple members in a cloud environment. At the same time, multiple clients want to use the computing power of the cloud server to perform machine learning and data mining, collaboratively solve optimization problems and obtain optimal results. However, uploading user data to the server and training the model on the server may lead to serious user privacy disclosure. For example, in 2021, the registration information of Alibaba Cloud users was leaked, in 2022, 37 GB source code of Microsoft was leaked, and 170 million data of super star learning APP was illegally sold. The losses caused by global cybercrime more than 6 trillion in 2021, about six times as much as in 2020. With frequent privacy disclosure, the public pays more attention to privacy protection. In order to make better use of cloud services, we need a method to process data safely in the cloud environment.

Conclusion

Considering the diversity of users in the cloud computing environment, MKHE supports information storage and sharing and secure homomorphic calculation from different users. It is more suitable to solve privacy and security issues in the cloud computing, which is of great practical significance. However, the existing MKHE schemes weaken the functionality of MKHE in the expansion of ciphertext dimension and computational complexity. In this paper, we first improve the DGHV scheme [11] and propose a DGHV-type MKHE scheme. Compared with the original DGHV scheme, on the premise of ensuring security, an extended key is introduced to expand the function of the scheme. Compared with other MKHE schemes, our scheme is easier to understand in ciphertext composition and calculation form. In addition, in the experiment, the cubic form is used to reduce the public key size of the scheme to ?(? 3 ). Through experiments, we evaluate the generation time of the keys, storage capacity, expansion and homomorphic operation. However, introducing a trusted KGC when generating joint key may be difficult to meet in some practical applications. Whether to cancel the setting of KGC in joint key distribution is an important research content. We should also strive to make MKHE better applicable to the cloud environment.

1- Introduction

2- Model and principle

3- Results and discussion

4- Conclusions

References

Abstract

As an indirect imaging technique, computational ghost imaging (GI) obtains the object information by calculating the intensity correlation between a series of computer-generated matrices and the corresponding bucket signals, which thereby offers a potential application in optical encryption. Here, we propose a new steganography scheme, called ghost steganography, based on the principle of computational GI. In our ghost steganography scheme, the bucket intensity signals of a secret image are concealed into the ones of a non-secret image by applying a non-conspicuous number integration process. To further increase the security, we introduce RSA cryptography to encode the integrated bucket signals after the steganography process. Simulation and experiment results fully demonstrate the feasibility of our optical ghost cryptography and steganography scheme. Our work paves a way to the application of GI in steganography and also enriches the knowledge of symmetric and asymmetric optical cryptography.

Introduction

Ghost imaging (GI), also known as correlated imaging, is an indirect imaging modality which obtains the object information from the intensity fluctuation correlation of two beams. One beam, called object beam, going through the object, is measured by a bucket detector. The other beam, called reference beam, interacting without the object, is detected by a spatially resolved camera. GI was firstly achieved with entangled photon pairs experimentally in 1995 [1], and later extended into the classical region with various thermal light sources [2–11]. Different from the two-detector GI, Shapiro proposed the computational GI in 2008 [12], which generated the active illumination patterns by using the spatial light modulator instead of passive measurements of reference beam. Similar to the single-pixel camera technique [13], computational GI can recover the object image with only a single-pixel detector [14,15], which largely simplifies the experimental setup. Meanwhile, the correlation between the computer-generated random matrix and the object beam intensity of computational GI offered a potential application in optical encryption [16-30]. To increase the efficiency and security of optical encryption based on GI [16], different methods are proposed and developed, including gray-scale and color encryption [17], multiple-image encryptions [18,26,29], specific phase masks schemes [22,25,27,30], XOR operation scheme [24].

1- Introduction

2- Internet of things technology: towards to a new era

3- Privacy and cryptography primitives

4- Security level and implementation devices

5- Selected IoT board 6. Proposed cryptographic system

7- Proposed IoT system implementation

8- Implementations results and comparisons

9- Conclusions & outlook

References

Abstract

With each passing day, Internet of Things (IoT), has the potential to transform our society to a more digital way. In this paper, a cryptographic system is proposed, which has been designed and implemented, following the IoT optimized technologies. As the benefits of IoT are numerous, the need for a privacy platform is more than necessary to be developed. This work aims to demonstrate this by, firstly, implementing efficient and flexible, the fundamentals primitives of cryptography and privacy. Secondly, this is achieved, by introducing applied cryptography, in a more interactive and flexible approach. The proposed system and the incorporation of this platform is scrutinized. In the context of this work, an application of symmetric cryptography is introduced, based on the Advanced Encryption Standard (AES) in Electronic Code Book (ECB), Cipher Block Chaining (CBC) and Counter (CTR) modes of operation, for both encryption and decryption of texts, images and electronic data applications. In addition two other security schemes are supported by the proposed system: AES Galois/Counter Mode (GCM) and AES Galois Message Authentication Code (GMAC). The GCM proposed integration, in an authentication scheme, designed to provide authenticity and confidentiality, at the same time. On the other hand, GMAC, can be applied as message authentication code. Both operations, are optimized in sense of implementation resources, since the major cost is targeted to AES core. In addition, based on the integrated hardware modules, user registration and validation is proposed and implemented, with no additional cost, and with no performance penalty. Furthermore, two factor authentication has been designed and proposed, based on One Time Passwords (OTP), which can been produced with a random procedure. After these, a reference to the security levels, as regards to the communication between the IoT layers of the architecture, is presented. IoT hardware platforms are facing lack of security level and this brings the opportunity to use advanced security mechanisms. Implementation comparison results emphasize the importance of testing and measuring the performance of the alternative encryption algorithms, supported by hardware platforms.

Introduction

Nowadays, the development of Internet is being shaped mostly by instant data exchange. The pattern of device usage in an extensive and exhaustive way, is giving ability to the users, in order to communicate and share information. For this reason, the need for protecting the devices and the information dissemination among them, is determined as mandatory. Internet of Things (IoT), can be described as an application domain that integrates different technological and social fields [1]. Furthermore, IoT can bring together cryptographic models and security schemes, for implementation purposes, as described in [2]. For example, IoT systems can be based on AES, ECB and CBC modes of operation for symmetric encryption. Other algorithms such as RSA, SHA standards DiffieHellman and Elliptic Curve Cryptography (ECC), can serve as supplemented privacy schemes, for asymmetric cryptography. Embedded systems design, are usually based on microprocessors, and microcontrollers. The role of embedded systems is to perform specific tasks such as sampling sensors and provide cost effectiveness and high portability, due to their small sizes. It is worthwhile mentioning, that these devices are discretized in how they interpret data. Moreover, they can be utilized for different applications as they are able to be programmable, flexible and can further be applicable to IoT environments.

Internet of Things (IoT) ideates smart and inter-connected things capable of sharing their perceptions through the Internet. These devices are different from conventional Internetconnected devices in the sense that these are able to perform skill-full things on their own with minimal or no human interaction. Unfortunately, with the advent of amalgamate technologies, security has become a major concern for IoT networks. Recent efforts include re-inventing cryptographic solutions through the use of light-weight operations. However, after witnessing the growth of quantum computers, it can be inferred that the cryptographic techniques based on mathematical problems are not reliable enough. Therefore, there is need to develop solutions that can easily resist the adversarial effects and are suitable for the post-quantum world. In this paper, we perform in-depth analysis over the role of post-quantum cryptographic techniques for securing IoT networks and also explore ongoing research efforts in the field. In addition, we discuss the open research challenges and future research directions in the field.

Introduction

Internet of Things (IoT) is a cluster of technologies that requires different set of protocols, infrastructures, data storage mechanisms, and ways of communication associated with Information Technology (IT). Resource allocation in a smart manner is one of the key requirements while dealing with IoT devices as they are constrained in terms of energy, storage and computational resources. Communication technology for actuating and managing the data is also of great concern [1–3]. As a matter of fact, IoT ecosystem faces a lot of challenges associated with a number of aspects as shown in Fig. 1. This new paradigm of connected objects needs an assimilation of next generation technologies. In order to simultaneously provide ultra-reliability of communication, following technologies can be integrated – • Software Defined Networks (SDN) for managing haptic communications, • 5G technology for providing tactile Internet, and • The new era of distributed Cloud computing, i.e., edge computing for managing the resources. Significant increase in the use of IoT devices is bringing numerous business opportunities. However, manufacturers are still unable to ensure their customers that these devices are secure. Hence, despite all of the advantages, the security issues of these devices are acting as a huge rock in the way of letting this paradigm making a colossal impact on our lives.

1- Introduction

2- Related works

3- Preliminaries

4- Problem statement

5- Cloud-based multi-authority ABE access control and offline proxy re-encryption

6- Security analysis

7- Performance analysis

8- Discussion

9- Conclusion

References

Abstract

This paper addresses the always online behavior of the data owner in proxy re-encryption schemes for re-encryption keys issuing. We extend and adapt multi-authority ciphertext policy attribute based encryption techniques to type-based proxy re-encryption to build our solution. As a result, user authentication and user authorization are moved to the cloud server which does not require further interaction with the data owner, data owner and data users identities are hidden from the cloud server, and re-encryption keys are only issued to legitimate users. An in depth analysis shows that our scheme is secure, flexible and efficient for mobile cloud computing.

Introduction

Mobile cloud computing enables mobile devices to perform heavy resource-demanding tasks thanks to the availability of cloud-based resources through heterogeneous wireless networks. An exhaustive description of mobile cloud computing (MCC) architecture can be found in [1]. As more mobile devices are increasingly being used nowadays to store and process personal and corporate data [2], there are growing concerns regarding the privacy and confidentiality of sensitive data as the device can be stolen, compromised or hacked. Moreover, applications running on the mobile device should consume the least possible amount of energy. Therefore, any security solution designed for mobile cloud data storage should be mobile device resource-friendly. The main drawback of outsourcing data to the cloud is that sensitive data can be accessed by a breached cloud service provider (CSP), as well as by some unauthorized users, leading to a confidentiality breach. Encryption has been proposed as a solution to secure data. However, only a limited number of operations can be performed on encrypted data, with one operation of interest being the search over encrypted cloud data as described in [3]. Requiring the user to download all the encrypted data locally before attempting decryption incurs many inconveniences among which, a high data transmission overload and, due to the cloud computing principle of pay-as-you-go, high financial expenditures. Such scheme additionally poses a problem of user authentication and authorization as arbitrary ciphertexts from different data owners can directly be accessed by any user without prior authorization mean.

1- Introduction

2- Preliminaries

3- Our system model

4- The detailed scheme

5- Security and performance analysis

6- Conclusion

References

Abstract

The mechanism for traditional searchable symmetric encryption (SSE) is pay-then-use. This situation is not fair to user because the cloud server may return wrong results. Besides, the user needs to verify these results locally. In order to ensure fairness and reduce user’s calculations, we combined blockchain with SSE, and proposed a fair SSE scheme based on blockchain. Our scheme can guarantee fairness for both parties. That is, if the user is not honest, he cannot get right results from the server, and at the same time the server cannot get any information related to the plaintexts during this search process. If the server is not honest, except for the service charge, it will be punished automatically. Moreover, the user in our scheme does not need to verify the results locally. The security and performance analyses showed our scheme was semantic secure and feasible.

Introduction

With the rapid development of cloud computing, more and more service providers have issued a series of cloud products, such as Amazon Web Services, Google Cloud, etc. These cloud storage systems have changed the original storage way of data. Users can store their data on the cloud directly, and then get access to them on any device conveniently. The data usually include some sensitive information, such as name, telephone number and so on. The cryptography plays an important role in protecting data privacy. Namely, to protect the privacy of data, users can encrypt them before uploading. However, which encryption algorithm is chosen will affect the search efficiency. Searchable symmetric encryption (SSE) was firstly proposed by Song et al. [1]. It allows a user to outsource his data to the cloud in a private manner, while maintains the ability of selectively search segments of the data. It involves three participants in SSE: Data owner, cloud server and user. The data owner encrypts documents D1,D2, . . .,Dn and stores them on the cloud. The cloud server manages these ciphertexts and provides search service to users. If a user gets the permission from the data owner, he can obtain search results with the help of the cloud server by using an encrypted keyword. Finally, the user decrypts them locally. There are two ways to construct SSE. The first is to use an index, and the second is not to use an index. Because the former can improve search efficiency, the subsequent SSE works mostly accept this approach. A SSE scheme is secure if anyone except authorised person cannot learn any information about the plaintexts when he/she only gets ciphertexts.

Nowadays, the management and analyses of ‘big data’ are becoming indispensable for numerous organizations all over the world. In many cases, multiple organizations want to perform data analyses on their combined databases. Skyline query is one of the popular operations for selecting representative objects from a large database, where any other object within the database does not dominate each of the representative objects, called ‘skyline’. Like other data analytics operations, the multi-party skyline query can provide benefits to the participating organizations by retrieving the skyline objects from their combined databases. Such a multi-party skyline query demands the disclosure of individual parties’ objects to others during the computation. But, owing to the data privacy and security concern of the present IT era, such disclosure of the individual parties’ databases is strictly prohibited. Considering this issue, we are proposing a new framework for the privacy-preserving multi-party skyline query, exploiting additive homomorphic encryption along with data anonymization, perturbation, and randomization techniques. The underlying protocols within our proposed framework ensure that every participating party can identify its multi-party skyline objects without revealing the objects to others during the multi-party skyline query. The detailed privacy and security analyses show that the proposed framework can achieve the desired computation goal without privacy leakage. Besides, the performance evaluation through complexity analyses, extensive simulations, and comprehensive comparison also demonstrate the utility and the efficiency of the proposed framework.

Introduction

Organizations throughout the world are producing a vast amount of data, known as ‘big data’. Consequently, the demand for big data analytics tools is growing rapidly. These tools have attracted massive attention to organizations and researchers for making strategic decisions and for new knowledge acquisitions. Open market product pricing, risk management in investment, consumer buying pattern analysis, financial transaction analysis, health data analysis, etc. are remarkable examples of big data analyses. Still, big data is introducing new challenges for collection, storage, process, analyze, etc. In the current trend of IT, multiple organizations dealing with similar kind of services are collecting compatible big data, and have noticed the importance of analytical results that can be found from the union of their databases. Such sort of joint data analyses requires multi-party computation over the combined databases of all organizations. Since many organizational databases may contain various sensitive data like personal or financial data, revealing these data can seriously violate the individuals’ privacy and can be the reason of significant financial and goodwill loss for the organizations.

Outsourcing image search services to public clouds is an ever-increasing trend. However, directly outsourcing image datasets to untrusted clouds introduces privacy concerns. Several secure image retrieval schemes have been proposed recently. However, most of them require participation from image owners when building secure indexes, which wastes many computational resources of the image owners. Several schemes are proposed to solve this problem, but they suffer from low search accuracy on large datasets. In this paper, we propose the first secure image retrieval scheme that simultaneously solves these two problems. To obtain higher search accuracy, we extract image features via fine-tuned convolutional neural networks. Then, the image features are encrypted by using the secure k-Nearest Neighbor algorithm. To improve search speed and reduce the cost of image owners, we let cloud servers locally build a secure hierarchical index graph by using the encrypted image features. Besides, the secure index can be built and updated in parallel. We provide security analysis for the proposed scheme. Performance evaluations on the CIFAR-10 dataset show that the proposed scheme is practical. Moreover, compared with a recent scheme, our scheme can save more index construction time and cost of image owners when building secure indexes.

Introduction

With the rapid development of multimedia devices, a great many images are created every day. Image retrieval is a promising technology that helps us quickly find the images we are interested in. Content-based image retrieval (CBIR) plays a vital role in image retrieval. CBIR aims to use the visual content of a query image to search similar images from an image database, which is useful in many fields, such as remote diagnosis [1], face recognition [2], and online shopping [3]. Concerning the huge storage and the complicated maintenance cost, more and more image owners prefer to outsource huge image datasets and image search services to public clouds. Then, authorized users can retrieve similar images from the clouds. Unfortunately, although the public clouds reduce the cost of the image owners, they also introduce new security threats [4]. Since the outsourced image datasets may contain sensitive information, directly outsourcing the image datasets to untrusted cloud servers may cause the sensitive images to be stolen by commercial opponents, hackers, and cloud providers. Using traditional cryptosystems to protect the images can avoid the disclosure of the sensitive information, but the public clouds can no longer provide image search services.

In this paper, we propose a fast encoding method to facilitate an affine motion estimation (AME) process in versatile video coding (VVC) encoders. The recently-launched VVC project for next-generation video coding standardization far outperforms the High Efficiency Video Coding (HEVC) standard in terms of coding efficiency. The first version of the VVC test model (VTM) displays superior coding efficiency yet requires higher encoding complexity due to advanced inter-prediction techniques of the multi-type tree (MTT) structure. In particular, an AME technique in VVC is designed to reduce temporal redundancies (other than translational motion) in dynamic motions, thus achieving more accurate motion prediction. The VTM encoder, however, requires considerable computational complexity because of the AME process in the recursive MTT partitioning. In this paper, we introduce useful features that reflect the statistical characteristics of MTT and AME and propose a method that employs these features to skip redundant AME processes. Experimental results show that—when compared to VTM 3.0—the proposed method reduces the AME time of VTM to 63% on average, while the coding loss is within 0.1% in the random-access configuration.

Introduction

The amount of video data has increased rapidly, especially with the growing use of Internet-based streaming services and devices that receive video broadcasts. The bandwidth and storage capacity of video applications is limited, requiring efficient video compression techniques. This need for video compression will further increase due to the higher resolutions of volumetric content such as 360-degree and high dynamic range (HDR) videos. Considering this diverse and growing demand for more powerful compression, a new video coding standardization project called versatile video coding (VVC) was launched recently by the Joint Video Exploration Team (JVET) of two expert groups: ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG). The JVET published the initial draft of VVC in 2018 [1] and released the VVC test model (VTM). VTM has a similar structure to the High Efficiency Video Coding (HEVC) test model (HM), but it uses advanced tools that provide better compression performance. A key concept among these tools is the multiple-type tree (MTT) segmentation structure [2].