Ciphertext Policy Attribute based Homomorphic Encryption (CP-ABHERLWE): a fine-grained access control on outsourced cloud data computation

Recently, homomorphic encryption is becoming one of the holy grail in modern cryptography research and serve as a promising tools to protect outsourced data solutions on cloud service providers. However, most of the existing homomorphic encryption schemes are designed to achieve Fully Homomorphic Encryption that aimed to support arbitrary computations for only single-data ownership scenario. To bridge these gaps, this paper proposed a non-circuit based Ciphertext Policy-Attribute Based Homomorphic Encryption (CP-ABHER-LWE) scheme to support outsourced cloud data computations with a fine-grained access control under the multi-user scenario. First, this paper incorporates Attribute Based Encryption (ABE) scheme into homomorphic encryption scheme in order to provide a fine grained access control on encrypted data computation and storage. Then, the proposed CP-ABHER-LWE scheme is further extended into non-circuit based approach in order to increase the practical efficiency between enterprise and cloud service providers. The result shows that the non-circuit based CP-ABHER-LWE scheme has greatly reduced the computation time and ciphertext size as compared to circuit based approach. Subsequently, the proposed CP-ABHER-LWE scheme was proven secure under a selective-set model with the hardness of Decision Ring-LWEd,q,ई problem

Securing Big Data Processing With Homomorphic Encryption

The arrival of Big Data era has challenged the conventional end-to-end data protection mechanism due to its associated high volume, velocity and variety characteristics. This paper reviews the security mechanisms of dominated Big Data processing platform – Hadoop and examines its capabilities on providing the end-to-end data protection: data-in-transit, data-at-rest and data-in-transform. While Hadoop is limited to protect data-in-transit with its built-in security mechanism and relies on third-party vendor tools (e.g. HDFS disk level encryption or security-enhanced Hadoop security distribution) for securing data-at-rest, the homomorphic encryption scheme that capable of performing computation on encrypted data serve as a promising tool to provide end-to-end data protection Big Data processing. However, existing circuit-based homomorphic encryption schemes still insufficient enough for supporting Big Data applications due to their high complexity of computation, huge generated ciphertext and public key size. To address this problem, this paper proposed homomorphic encryption from a non-circuit-based approach. Our result shows that the newly proposed non-circuit based homomorphic encryption has greatly reduced the computation time and ciphertext size as compared to existing circuit-based homomorphic encryption schemes, therefore amenable to support the high volume and high-velocity requirement of Big Data processing

Development of a diversified ensemble data summarization (DDS) tool for learning medical data in a multi relational environment

Medical or scientific data are normally stored in relational databases in which data are stored in multiple tables. A data summarization approach to knowledge discovery in structured medical datasets is often limited due to the complexity of the database schema. Since most of these data are stored in multiple tables, designing a suitable data summarization method for each individual table that is associated with the target table is required in order to get the best result in summarizing the overall data stored in a multi-relational environment. A diversified data summarization ensemble method is best applied in the task of learning data stored in multiple tables since ensemble methods improve quality and robustness of the results. This research investigates the feasibility of combining a few types of data summarization methods ( e.g., DARA) in order to learn data stored in relational databases with high cardinality attributes (one-to-many relations between entities). The proposed algorithm is called a diversified data summarization ensemble method. With this new algorithm, one could facilitate the task of data modelling for data stored in a multi-relational setting by improving the predictive accuracy of the data modelling task. This can be achieved by summarizing each table that exists in the database by using a more appropriate data summarization method depending on the type of data stored in each individual table. This research helps the understandi'ng and development of a diversified data summarization ensemble method that is able to summarize relational data. By applying a subset of data summarization methods to summarize different sets of the relational datasets, more interpretable and useful information can be extracted

A survey of homomorphic encryption for outsourced big data

With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation

Separable Reversible Data Hiding in Encryption Image with Two-Tuples Coding

Separable Reversible Data Hiding in Encryption Image (RDH-EI) has become widely used in clinical and military applications, social cloud and security surveillance in recent years, contributing significantly to preserving the privacy of digital images. Aiming to address the shortcomings of recent works that directed to achieve high embedding rate by compensating image quality, security, reversible and separable properties, we propose a two-tuples coding method by considering the intrinsic adjacent pixels characteristics of the carrier image, which have a high redundancy between high-order bits. Subsequently, we construct RDH-EI scheme by using high-order bits compression, low-order bits combination, vacancy filling, data embedding and pixel diffusion. Unlike the conventional RDH-EI practices, which have suffered from the deterioration of the original image while embedding additional data, the content owner in our scheme generates the embeddable space in advance, thus lessening the risk of image destruction on the data hider side. The experimental results indicate the effectiveness of our scheme. A ratio of 28.91% effectively compressed the carrier images, and the embedding rate increased to 1.753 bpp with a higher image quality, measured in the PSNR of 45.76 dB

Recent Technologies, Security Countermeasure and Ongoing Challenges of Industrial Internet of Things (IIoT): A Survey

The inherent complexities of Industrial Internet of Things (IIoT) architecture make its security and privacy issues becoming critically challenging. Numerous surveys have been published to review IoT security issues and challenges. The studies gave a general overview of IIoT security threats or a detailed analysis that explicitly focuses on specific technologies. However, recent studies fail to analyze the gap between security requirements of these technologies and their deployed countermeasure in the industry recently. Whether recent industry countermeasure is still adequate to address the security challenges of IIoT environment are questionable. This article presents a comprehensive survey of IIoT security and provides insight into today’s industry countermeasure, current research proposals and ongoing challenges. We classify IIoT technologies into the four-layer security architecture, examine the deployed countermeasure based on CIA+ security requirements, report the deficiencies of today’s countermeasure, and highlight the remaining open issues and challenges. As no single solution can fix the entire IIoT ecosystem, IIoT security architecture with a higher abstraction level using the bottom-up approach is needed. Moving towards a data-centric approach that assures data protection whenever and wherever it goes could potentially solve the challenges of industry deployment

Key Policy-Attribute Based Fully Homomorphic Encryption (KP-ABFHE) Scheme for Securing Cloud Application in Multi-users Environment

Recently, cloud technologies has become a cost-effective data solution among the small and medium-sized enterprises (SMEs). However, there is a raising concern on its security. This paper proposed the Key Policy-Attribute Based Fully Homomorphic Encryption (KP-ABFHE) scheme for providing an end-to end data protection in multi-users cloud environments. The proposed KP-ABFHE scheme is able to perform the computation while providing fine-grained access on the encrypted data. The proposed scheme is able to handle a monotonic access structure over a set of authorized attributes, without sacrificing the computation capabilities of homomorphic encryption. In addition, this paper proves that the proposed scheme is secure under a selective-set model with the hardness of Decision Ring-LWE${_{d,q, \chi } }$ problem

Research on the security algorithm of reversible information hiding in communication encrypted image

Considering the embedding capacity and the security of the algorithm, the paper proposes a reversible information hiding algorithm for encrypted images based on. The information concealer can find the transformed pixel group in the encrypted image according to the same key and use the embedding key to replace the corresponding pixel's LSB to complete the reversible embedding of secret information. Simulation tests show that the proposed hiding method can realize the lossless recovery of the carrier image and the original image, which effectively reduces the transmission load and has high security

A review of phishing email detection approaches with deep learning algorithm implementation

Phishing email is designed to mimics the legitimate emails to fool the victim into revealing their confidential information for the phisher's benefit. There have been many approaches in detecting phishing emails but the whole solution is still needed as the weaknesses of the previous and current approaches are being manipulated by phishers to make phishing attack works. This paper provides an organized guide to present the wide state of phishing attack generally and phishing email specifically. This paper also categorizes machine learning into shallow learning and deep learning, followed by related works in each category with their contributions and drawbacks. The main objective of this review is to uncover the utility of machine learning in general, and deep learning in particular, in order to detect phishing email by studying the literature. This will provide an insight of the phishing issue, the alternatives prior to the phishing email detection and the contrast of machine learning and deep learning approaches in detecting phishing emails

(E)-2-(3,4-Dimeth­oxy­benzyl­idene)-5,6-dimeth­oxy-2,3-dihydro-1H-inden-1-one

In the title compound, C20H20O5, the 2,3-dihydro-1H-indene ring system is essentially planar [maximum deviation = 0.010 (1) Å] and is inclined at an angle of 4.09 (4)° with respect to the phenyl ring. The C=C bond has an E configuration. In the crystal, the mol­ecules are linked into chains propagating in [102] via inter­molecular C—H⋯O hydrogen bonds. The crystal structure is further consolidated by C—H⋯π inter­actions