From Single to Multi-clouds Computing Privacy and Fault Tolerance

AbstractSecurity issues of data hosted in a Cloud Computing provider remain hidden seen excessive marketing that led to a totally unrealistic view of cloud computing security. Although Cloud Computing has not yet reached the level of maturity expected by its customers, and that the problems of confidentiality, integrity, reliability and consistency (CIRC) are still open, the researchers in this field have already considered a future cloud strategy which aims: a better QoS, reliability and high availability, it is the Multi-Clouds, Cloud of Clouds or Interclouds.This paper will present the security limitations in the single Cloud and the usefulness of adopting rather Multi-Clouds strategy to reduce security risks, through the use of DepSky which is a virtual storage system that ensures better availability and high confidentiality of data

Homomorphic encryption and some black box attacks

This paper is a compressed summary of some principal definitions and concepts in the approach to the black box algebra being developed by the authors. We suggest that black box algebra could be useful in cryptanalysis of homomorphic encryption schemes, and that homomorphic encryption is an area of research where cryptography and black box algebra may benefit from exchange of ideas

Cloud Storage Protection Scheme Based on Fully Homomorphic Encryption

Cloud computing allows enterprises and individuals to have a less physical infrastructure of software and hardware. Nevertheless, there are some concerns regarding privacy protection which may turn out to be a strong barrier. Traditional encryption schemes have been used to encrypt the data before sending them to the cloud. However, the private key has to be provided to the server before any calculations on the data. To solve this security problem, this paper proposes a fully homomorphic encryption scheme for securing cloud data at rest. The scheme is based on prime modular operation, its security depends on factoring multiple large prime numbers (p1, p2,...pn) up to n, which is formed from very large prime numbers up to hundreds of digits as this is an open problem in mathematics. In addition, the elements of the secret key are derived from a series of mathematical operations and the calculation of an Euler coefficient within the modular of integers. Furthermore, it adds the complexity of noise to the plaintext using the number of users of the Cloud Service Provider. Moreover, its randomness is evaluated by the National Institute of Standards and Technology statistical tests, and the results demonstrating that the best statistical performance was obtained with this algorithm

From cloud computing security towards homomorphic encryption: A comprehensive review

“Cloud computing” is a new technology that revolutionized the world of communications and information technologies. It collects a large number of possibilities, facilities, and developments, and uses the combining of various earlier inventions into something new and compelling. Despite all features of cloud computing, it faces big challenges in preserving data confidentiality and privacy. It has been subjected to numerous attacks and security breaches that have prompted people to hesitate to adopt it. This article provided comprehensive literature on the cloud computing concepts with a primary focus on the cloud computing security field, its top threats, and the protection against each one of them. Data security/privacy in the cloud environment is also discussed and homomorphic encryption (HE) was highlighted as a popular technique used to preserve the privacy of sensitive data in many applications of cloud computing. The article aimed to provide an adequate overview of both researchers and practitioners already working in the field of cloud computing security, and for those new in the field who are not yet fully equipped to understand the detailed and complex technical aspects of cloud computing

Trustworthy Cloud Computing

Trustworthy cloud computing has been a central tenet of the European Union cloud strategy for nearly a decade. This chapter discusses the origins of trustworthy computing and specifically how the goals of trustworthy computing—security and privacy, reliability, and business integrity—are represented in computer science research. We call for further inter- and multi-disciplinary research on trustworthy cloud computing that reflect a more holistic view of trust

Applicability evaluation of experimental approaches for preserving privacy in cloud

Among several proposed solutions for private processing in cloud computing, perhaps the most promising class of approaches is privacy-preserving computation. This thesis reviews existing approaches for private processing and demonstrates practical use of one such novel approach for privacy-preserving computation, fully homomorphic encryption (FHE). FHE allows arbitrary computations on ciphertexts and yields a result, which, when decrypted, is the same as if they were performed on corresponding plaintexts. We develop a simple web bank cloud application that uses FHE to preserve privacy of banking transactions. In order to support FHE client-side, we produce two architecturally different setups that can be used with the same web application. Furthermore, we evaluate and discuss their practical applicability in the cloud according to predefined metrics. Our results indicate that even for trivial use-cases, where performance of encrypted processing, encryption or decryption is not a limiting factor, the main factors preventing broader adoption of FHE at present are significant communication and initialization overheads both client and server-side, lack of support for several high level programmatic routines and lack of developer-friendly frameworks. To the best of our knowledge, some of these issues have not yet been addressed in the literature, as this thesis is one of the early attempts to bring FHE to the web

Privacy preserving algorithms for newly emergent computing environments

Privacy preserving data usage ensures appropriate usage of data without compromising sensitive information. Data privacy is a primary requirement since customers' data is an asset to any organization and it contains customers' private information. Data seclusion cannot be a solution to keep data private. Data sharing as well as keeping data private is important for different purposes, e.g., company welfare, research, business etc. A broad range of industries where data privacy is mandatory includes healthcare, aviation industry, education system, federal law enforcement, etc.In this thesis dissertation we focus on data privacy schemes in emerging fields of computer science, namely, health informatics, data mining, distributed cloud, biometrics, and mobile payments. Linking and mining medical records across different medical service providers are important to the enhancement of health care quality. Under HIPAA regulation keeping medical records private is important. In real-world health care databases, records may well contain errors. Linking the error-prone data and preserving data privacy at the same time is very difficult. We introduce a privacy preserving Error-Tolerant Linking Algorithm to enable medical records linkage for error-prone medical records. Mining frequent sequential patterns such as, patient path, treatment pattern, etc., across multiple medical sites helps to improve health care quality and research. We propose a privacy preserving sequential pattern mining scheme across multiple medical sites. In a distributed cloud environment resources are provided by users who are geographically distributed over a large area. Since resources are provided by regular users, data privacy and security are main concerns. We propose a privacy preserving data storage mechanism among different users in a distributed cloud. Managing secret key for encryption is difficult in a distributed cloud. To protect secret key in a distributed cloud we propose a multilevel threshold secret sharing mechanism. Biometric authentication ensures user identity by means of user's biometric traits. Any individual's biometrics should be protected since biometrics are unique and can be stolen or misused by an adversary. We present a secure and privacy preserving biometric authentication scheme using watermarking technique. Mobile payments have become popular with the extensive use of mobile devices. Mobile applications for payments needs to be very secure to perform transactions and at the same time needs to be efficient. We design and develop a mobile application for secure mobile payments. To secure mobile payments we focus on user's biometric authentication as well as secure bank transaction. We propose a novel privacy preserving biometric authentication algorithm for secure mobile payments

Data Privacy and Trust in Cloud Computing

This open access book brings together perspectives from multiple disciplines including psychology, law, IS, and computer science on data privacy and trust in the cloud. Cloud technology has fueled rapid, dramatic technological change, enabling a level of connectivity that has never been seen before in human history. However, this brave new world comes with problems. Several high-profile cases over the last few years have demonstrated cloud computing's uneasy relationship with data security and trust. This volume explores the numerous technological, process and regulatory solutions presented in academic literature as mechanisms for building trust in the cloud, including GDPR in Europe. The massive acceleration of digital adoption resulting from the COVID-19 pandemic is introducing new and significant security and privacy threats and concerns. Against this backdrop, this book provides a timely reference and organising framework for considering how we will assure privacy and build trust in such a hyper-connected digitally dependent world. This book presents a framework for assurance and accountability in the cloud and reviews the literature on trust, data privacy and protection, and ethics in cloud computing

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed