Distributed computing framework in security: Case study of encryption method

Security and privacy are the biggest obstacles in Database as a service (DBaaS) of Cloud Computing. In DbaaS, cloud service providers provide services for storing customers data. As the data are managed by an un-trusted server, the service is not fully trustworthy. The data at the third party data center can be made secure by encrypting the database. But querying the encrypted database is not easy. The result can be obtained from the encrypted database either by decrypting the database for every query or the query itself is encrypted and encrypted query is executed over encrypted database. Another problem associated with most of the database encryption algorithms is that they do not support range query. The proposed framework performs database encryption, query encryption and also supports range query over encrypted databases. This framework is focused on securing database as well as storing sensitive information without any leaks. A double layered encryption is used for sensitive data and a single layer encryption is used for non-sensitive data. Order Preserving Encryption (OPE) is used for single layer encryption. OPE maintains the order in encrypted database and so range query can be performed over encrypted database using encrypted query. OPE has a drawback of revealing information and so for sensitive data, a double layered encryption using Format Preserving Encryption (FPE) followed by OPE symmetric key encryption algorithm is proposed. Symmetric key is used for both OPE and FPE but key is divided into two parts for double encryption.

As the rapid development of hardware and network technology, cloud computing has become an important research topic. For applications of large-scale data processing, such as data warehouse, Map Reduce is the most famous platform for parallel data processing in cloud computing. To support the star-join queries in data warehouse, Scatter-Gather-Merge (SGM) proposes an efficient algorithm on the Map Reduce framework. However, SGM supports only the equi-join queries. Nonequi-join queries may cause SGM to fail. In this paper, we propose a method to cope with theta-join queries, i.e., both equi-join and nonequi-join queries. Our proposed method uses a novel manipulation of keys for partitioning data. The key manipulation matches up the Map Reduce paradigm, and makes theta-join queries workable on the Map Reduce platform. Our experimental results show that the proposed method achieves similar performance to SGM, but our method supports more join-query types. Our method performs even better than SGM in some query types of high data selectivity.

Secure multiparty computation protocol based on homomorphic encryption and its application in blockchain

Requirements engineering (RE) is the most difficult and important stage of any business process or project development. This research endeavors to find out the characteristics and aspects of requirements engineering enforced by cloud computing. Business Process Modeling Notation (BPMN) has made an impact in the respect to capture the process and to make the changes accordingly for improvement in business operations. This chapter defines how BPMN can be used as a method of requirements engineering in cloud business operations. Furthermore, this chapter presents the requirements engineering framework for service and cloud computing (BPMN-REF-SCC) and will also discuss the reference architecture for service and cloud computing. Finally, the research delivers a case of financial cloud business, which has developed 15 hypotheses for the validation and evaluation through simulation.

Machine learning is now playing important roles in daily lives, however, the privacy leakages are increasingly getting serious in the meantime. Current solutions to the privacy issues in machine learning, like differential privacy or homomorphic encryption either could only be applied to some specific scenarios or bring huge modification to the model construction, not to mention massive efficiency loss. In this paper, we consider addressing the privacy issue in machine learning from another perspective, without modification to models or severe efficiency loss. We proposed a straightforward privacy preserving machine learning scheme, training machine learning models directly over encrypted data. Ideally, this scheme could provide privacy protection to both training data and test data. We gave it a try by applying order preserving encryption (OPE) to the scheme. We discussed the possibility of using OPE to reveal the order information confidentially for model training. Several OPE algorithms were chosen to utilize the proposed method. Finally, comprehensive experiments were deployed on both synthetic and real datasets. The experiments on real datasets show that the learning performance of several well-known classifiers on before and after encryption changes slightly. The experiments on synthetic datasets show the classifier performance could be ranked according to fidelity and reliability.

With the wide adoption of cloud computing paradigm, it is important to develop appropriate techniques to protect client data privacy in the cloud. Encryption is one of the major techniques that could be used to achieve this goal. However, data encryption at the rest alone is insufficient for secure cloud computation environments. There is also the need for efficient techniques to carry out computation over encrypted data. Fully homomorphic encryption (FHE) and garbled circuits are naturally used to process encrypted data without leaking any information about the data. However, existing FHE schemes are inefficient for processing large amount of data in cloud and garbled circuits are one time programs and cannot be reused. Based on quaternion/octonion algebra and Jordan algebra over finite rings \(\mathbb {Z}_q\), this paper designs efficient fully homomorphic symmetric key encryption (FHE) schemes without bootstrapping (that is, noise-free FHE schemes) that are secure in the weak ciphertext-only security model assuming the hardness of solving multivariate quadratic equation systems and solving univariate high degree polynomial equation systems in \(\mathbb {Z}_q\). The FHE scheme designed in this paper is sufficient for privacy preserving computation in cloud.

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

Encryption of sensitive data is commonly used to preserve confidentiality and integrity. The problem with encrypting data is we have to decrypt it for applying operations on files such as altering a word document or querying a statistical database containing financial data. Decrypting the data will leave it vulnerable. If we want to perform conditional based search queries in an encrypted data entire data should be decrypted to perform it, unless an order preserving scheme is used. There is no homomorphic encryption scheme which will allow us to perform conditional based queries without decrypting the entire data. In mathematical term homomorphic describes the changeover of one data set into another upholding the association between elements in both sets. Order Preserving Encryption (OPE) schemes are used extensively used in cryptography because of their usage in database design. But there is a chances of inference attack in case of OPE. In this paper we had proposed and implemented a technique which will allow to perform conditional based search queries in the data encrypted using any encryption algorithm with log(n) decryption, thereby reducing complexity.

The increasing demand for Data Mining as a Service, using cloud storage, has raised data security concerns. Standard data encryption schemes are unsuitable because they do not support the mathematical operations that data mining requires. Homomorphic and Order Preserving Encryption provide a potential solution. Existing work, directed at data clustering, has demonstrated that using such schemes provides for secure data mining. However, to date, all proposed approaches have entailed some degree of data owner participation, in many cases the amount of participation is substantial. This paper proposes an approach to secure data clustering that does not require any data owner participation (once the data has been encrypted). The approach operates using the idea of an Encrypted Distance Matrix (EDM) which, for illustrative purposes, has been embedded in an approach to secure third-party data clustering - the Secure Nearest Neighbour Clustering (SNNC) approach, that uses order preserving and homomorphic encryption. Both the EDM concept and the SNNC approach are fully described.

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.

Secure Multi-Party Computation: Theory, practice and applications

As a new technique, cloud computing has a rapid development in recent years. However, the security problems have caused great influences to the development and popularization of cloud computing, the importance and urgency has not to be ignored. This paper introduces cloud computing and security situation, studies the main security problems of cloud computing, and comes up with a cloud computing security framework which can effectively solve these security problems, and points out that only to solve the security problems, cloud computing can unceasingly expanded, and the application will be more and more widely.KeywordsCloud computing securityFirewallData security

A logistics distribution vehicle scheduling model under cloud computing environment is established based on the analysis of factors affecting resource scheduling. The order information and logistics distribution vehicle information processing are completed under the framework of cloud computing, so as to obtain the most reasonable logistics distribution plan. To solve the problem of vehicle allocation in logistics distribution, a distribution path algorithm model and a minimum delivery cost algorithm model are established to provide the best strategy for logistics distribution scheme.

Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).

Cloud computing is providing cheaper, cost effective efficient automation, data management services to the organizations and this trend is gradually increasing. Using cloud based services can eliminate cost and complexity of managing the underlying application. It allows users to access and use the most sophisticated technologies without being required to pay enormous amounts of money to purchase the system or to develop it in-house. This study aimed to investigate cloud computing advantages and disadvantages, its services and implementation stages in pharmaceutical industry of Pakistan. Cloud computing practices included Cost Saving, Security & Privacy, Automation, Data Integrity, and Regulatory compliance. An Adopted survey was planned and circulated over an example of 400 workers. Research hypotheses were evaluated using structured equation modelling. The outcome shows that cost savings, security and privacy, competitive pressure, automation and awareness have a positive and fundamental effect on pharmaceutical operations. Strangely, there is a negative effect of external support on cloud computing. The effects are noticeable in the pharmaceutical industry.