PMDP: A Framework for Preserving Multiparty Data Privacy in Cloud Computing

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.

Multimedia processing with cloud is prevalent now, which the cloud server can provide abundant resources to processing various multimedia processing tasks. However, some privacy issues must be considered in cloud computing. For a secret image, the image content should be kept secret while conducting the multimedia processing in the cloud. Multimedia processing in the encrypted domain is essential to protect the privacy in cloud computing. Hu et al. proposed a novel framework to perform complex image processing algorithms in encrypted images with two cryptosystems: additive homomorphic encryption and privacy preserving transform. The additive homomorphic cryptosystem used in their scheme causes huge ciphertext expansion and greatly increases the cloud’s computation. In this paper, we modified their framework to a two-cloud scheme, and also implemented the random nonlocal means denoising algorithm. The complexity analysis and simulation results demonstrate that our new scheme is more efficient than Hu’s under the same denoising performance.

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge The main focus is to to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

Patient Health Record (PHR) is a complete Patient related health data, which is getting stored in cloud computing to gain cost benefit and better access control.In maintaining PHR, cloud computing plays an inevitable role, since small hospitals and physicians are not affordable to maintain own servers to keep PHRs for cost and security reasons .Providing accessibility to variable stake holders like Patients, Physicians, Hospitals and Insurance Providers, become a tedious process in private individual servers with encryption mechanisms.Cloud solution ensures that PHRs ' availability to the necessary stake holders at any point of time.In any country , there are laws which governs to maintain privacy of patient health records, and hence maintaining PHRs in cloud are subjected to privacy concerns and high risk of getting exploited.There are various encryption schemes exist to preserve PHR's security and privacy in Cloud computing.In this paper, we propose a secure PHR accessing scheme for dynamic environment to preserve privacy in semi trusted cloud servers .

In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user’s search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.

There are many definitions of the "cloud" and many historical and contemporary synonyms and subcategories (utility computing, grid computing, Platform-as-a-Service, etc.). All of these terms and ideas point toward the same trend: A consolidation of computing, storage, and networking resources within a single, typically shared infrastructure. Naturally, with many stakeholders and users operating side-by-side in a cloud environment, the security and privacy challenges are legion. But the news isn't all bad. Cloud computing also offers opportunities to improve security through the judicious use of virtualization, consolidation of security administrator duties, and general opportunities for rethinking security engineering in a new, centralized platform. Since its inception in 2009, the Cloud Computing Security Workshop (CCSW) has become the premier venue for research on security and privacy in cloud computing. It has also produced some of the most popular and highly cited papers in the CCS family of workshops, and drawn an excellent and diverse range of invited speakers. The workshop aims to bring together researchers and practitioners in all security aspects of cloudcentric and outsourced computing. Like CCS, its parent conference, CCSW offers a program spanning a broad range of topics and disciplines, including network security, consumer privacy, virtualization, secure hardware, and cryptography---to name just a few. This year, we received 28 full-paper submissions from 15 countries and five continents. All submissions received at least three reviews, and most received four. After extensive discussions and vigorous online debate, 11 submissions were accepted to appear at the workshop. These papers cover topics such as probing cloud infrastructure, securely outsourcing computation, authenticating storage using trusted hardware, secure code execution, auditing, and various flavors of encryption. This year also continued the CCSW tradition of high-profile invited speakers, with keynotes from Dr. Jesus Luna, Research Director at the Cloud Security Alliance, Dr. Marnix Dekker, Security Expert and Information Security Officer at the European Network and Information Security Agency (ENISA), and Slava Kavsan, Partner Security Development Manager (Azure) at Microsoft.

Cloud computing, thus, ensure more healthy demand for cloud services, certain measures against threats in order to protect information must be taken. In essence, the current paper seeks to extend knowledge regarding the improvement of paradigms of cloud computing with particular focus on the aspect of certification of authentic users and protection of the contents that are hosted in these environments. It mentions a new approach in the context of the authentication method that was deployed for the purpose of the AAA certification is presented there along with the watermarking and this RSA algorithms to enhance the security of the cloud file-sharing. This concept is far more effective than the conventional access policies that are top down and cal beforehand for systematic risk avoidance; hence, it helps to minimize the chances of an organization being vulnerable to various risks by right management. Iturbide’s data loss prevention technique eliminates the probability of losing sensitive information to the rest of the world as well as other unauthorized persons or organizations regaining access to privileged keys while Shamir’s secret sharing algorithm- polynomial interpolation ensures that the generation of keys is not a time-consuming exercise. In order to address the issues of persistent security threats in cloud computing paradigm, the proposed model includes computation, encryption and access to improve the overall security of cloud environment. However, these measures have been implemented, some of them can offer the best security when it comes to the ever-evolving threat. To maintain the privacy of clients’ data in the CL reinforcement learning, the paper suggests the use of homomorphic encryption for privacy-preserving RL inference. Therefore – as a result of the work – the necessity of implementing the suggested security model for the cloud computing environments as well as protecting the data and enhancing the level of trust in cloud solutions is stated. Keywords - QoS (Quality of Service), Energy Efficiency, Ontology, AAA (Authentication, Authorization, and Accounting), RSA Algorithm, RSA Algorithm, ReDCIM (Reconfigurable Digital Computing-In-Memory) Processor, tableopencache, LSTM (Long Short-Term Memory)

The success of cloud computing leads to large, centralized collections of virtual machine (VM) images. The ability to retrospect (examine the historical state of) these images at a high semantic level can be valuable in many aspects of IT management such as debugging and troubleshooting, software quality control, legal establishment of data or code provenance, and cyber forensics such as malware tracking and licensing violations. In this paper, we explore the privacy implications of VM retrospection. We argue that retrospection will worsen current concerns about privacy in cloud computing. We develop privacy-sensitive requirements for the design of a retrospection mechanism, and then show how they can be met in a functional prototype.

This paper analyzes the current problems in the data security and privacy of cloud computing. On the basis, this paper mainly research about the legitimacy and validity of research cloud computing process. First of all, this paper understand the concept of cloud computing technology, according to the working principle of the structure model of the cloud computing service analyzes the existence of data security and privacy. Cloud computing service providers for cost savings, or application access layer has invasion of illegal users, so cloud computing task submitted by the legitimate user will not be executed or not executed completely, and the users will get that a random outcome, and task submitted by illegal users may be was carried out. Research on the remote computing verification problem, analysis of the existing research on security work, found that the existing calculation in advance and the second validation of remote computing method for cloud computing users spending too big, this paper combines the characteristics of cloud computing mass data processing, puts forward the remote computing verification method based on the third-party certification, efficient and low consumption to ensure security.

Artificial Intelligence (AI) has attracted a large amount of attention in recent years. However, several new problems, such as privacy violations, security issues, or effectiveness, have been emerging. Differential privacy has several attractive properties that make it quite valuable for AI, such as privacy preservation, security, randomization, composition, and stability. Therefore, this paper presents differential privacy mechanisms for multi-agent systems, reinforcement learning, and knowledge transfer based on those properties, which proves that current AI can benefit from differential privacy mechanisms. In addition, the previous usage of differential privacy mechanisms in private machine learning, distributed machine learning, and fairness in models is discussed, bringing several possible avenues to use differential privacy mechanisms in AI. The purpose of this paper is to deliver the initial idea of how to integrate AI with differential privacy mechanisms and to explore more possibilities to improve AIs performance.

Significant research and development efforts in both industry and academia aim to improve the cloud's security and privacy. The author discusses related challenges, opportunities, and solutions.

The critical challenge of maintaining privacy in multi-query data processing environments, is a major concern, increasingly prevalent in the era of big data. The paper introduces the Key Aggregation in Trusted Execution for Multi-Query Privacy (KATE-MQP) architecture, a novel framework designed to securely handle, process, and analyze data across multiple queries while ensuring stringent privacy preservation. Utilizing differential privacy for key aggregation, the KATE-MQP model represents a significant advancement in the field. Methodologically, the architecture combines Trusted Execution Environments (TEEs) with sophisticated key management. The standout result of the KATE-MQP model is its ability to achieve an increased key aggregation accuracy by 40% while enhancing data privacy, as compared to existing models. Quantitatively, the model demonstrates a marked improvement in processing efficiency as the load increases, demonstrating its scalability. These results underscore the KATE-MQP architecture's potential as a scalable and efficient solution for secure data processing in various applications, from healthcare to finance, where data privacy and integrity are paramount.

Secure Multi-Party Computation: Theory, practice and applications

Cloud computing is a new computing model which uses virtualization technology, distributed computing, parallel computing and other existing technologies to achieve cloud service virtualization and economies of scale, whilst increasingly overwhelming cloud security issues has brought great challenges and concerns to the cloud services providers and cloud users, especially trust and privacy issues with regard to cloud computing and cloud shared storage associated security issues. In the paper, we expound the basic concepts of cloud computing, deployment models, service models and key features, analyze and outline the currently highlighted cloud security issues, report the status quo of cloud computing security, investigate the prevalent and typical cloud computing security problem key solving techniques, and thus render a comprehensive cloud computing security technical reference model, which is composed of associated cloud security solving techniques that result from inevitably multi-faceted cloud security issues. The model is expected to alleviate prominent cloud security issues. This paper generalizes cloud security technology research directions and further development space of cloud security technology and standardization.KeywordsCloud computingSecurity issuesTrusted cloud computing solving techniquesShared storage security solving techniquesCloud security technical reference model

Cloud computing has paved an excellent platform for the emergence of cost-effective technological solutions. However, security and privacy issues still remain as a stringent challenge during service catering. Explicitly, the service utility anomalies are liable to cause severe privacy and security issues in cloud service delivery. So, the overall performance of cloud service consumption and end-user applications’ service levels utility is degraded. The open access and distributed nature of the cloud computing is the major reason for its vulnerability to intruders. The security and privacy in cloud services have many challenges and problems still open for research. This paper proposes an intrusion detection method capable of detecting nine categories of attacks in two stages. This paper focuses on establishing a network-based intrusion detection mechanism using machine learning techniques. A model will be constructed with a supervised learning methodology using historical network flowdata and flowdata collected from the Internet.