Novel Proposed Work for Empirical Word Searching in Cloud Environment

People's lives have become much more convenient as a result of the development of cloud storage. The third-party server has received a lot of data from many people and businesses for storage. Therefore, it is necessary to ensure that the user's data is protected from prying eyes. In the cloud environment, searchable encryption technology is used to protect user information when retrieving data. The versatility of the scheme is, however, constrained by the fact that the majority of them only offer single-keyword searches and do not permit file changes.A novel empirical multi-keyword search in the cloud environment technique is offered as a solution to these issues. Additionally, it prevents the involvement of a third party in the transaction between data holder and user and guarantees integrity. Our system achieves authenticity at the data storage stage by numbering the files, verifying that the user receives a complete ciphertext. Our technique outperforms previous analogous schemes in terms of security and performance and is resistant to inside keyword guessing attacks.The server cannot detect if the same set of keywords is being looked for by several queries because our system generates randomized search queries. Both the number of keywords in a search query and the number of keywords in an encrypted document can be hidden. Our searchable encryption method is effective and protected from the adaptive chosen keywords threat at the same time

Blockchain-aided privacy-preserving medical data sharing scheme for e-healthcare system

Due to the massive applications of Internet of Things (IoT) and the prevalence of wearable devices, e-healthcare systems are widely deployed in medical institutions. As a significant carrier of medical data, electronic medical record (EMR) is convenient to be stored and retrieved, which greatly simplifies the experience of medical treatment and cuts down the trivial work of paramedics. However, EMRs usually include much sensitive information such as patients’ identification numbers or home addresses that may be easily captured by unauthorized doctors and cloud servers. Based on this concern, e-healthcare systems can make use of attribute-based encryption (ABE) to protect private information while achieving fine-grained access control of encrypted EMRs. Whereas, most ABE schemes do not support both policy hiding and keyword search. To address the above issues, we propose an inner product searchable encryption scheme with multi-keyword search (MK-IPSE) based on blockchain to provide full privacy preservation and efficient ciphertext retrieval for EMRs. Inner product encryption (IPE) can not only specify access permissions such that only users with matched attributes can get the target files, but also support access policy hiding. Besides, the proposed scheme combines searchable encryption (SE) and federated blockchain (FB) to implement efficient and stable multi-keyword search. Compared with the existing schemes, MK-IPSE shows better performance on computation and storage. Additionally, security analysis demonstrates that our scheme can resist IND-CKA and collusion attacks

Privacy-preserving query processing on health data

Due to the huge volume of digital data and the underlying complexity of data management, people and companies are motivated to outsource their computational requirements to the cloud. A significant portion of these productions are used in health applications. While popular cloud computing platforms provide flexible and low-priced solutions, unfortunately, they do so with little support for data security and privacy. This shortcoming clearly threatens sensitive data in cloud platforms. This is especially true for health information, which should always be adequately secured via encryption. Providing secure storage and access to health information that is generated by systems or used in applications, is the main challenge in today’s health care systems. As a result, owners of sensitive information may hesitate in purchasing such services, given the risks associated with the unauthorized access to their data. Considering this problem, researchers have recommended applying encryption algorithms. Data owners never disclose encryption keys in order to keep their encrypted data secure. Because cloud platforms can not search in data which is encrypted with regular encryption algorithms, it is supposed that data owners conceal their secrets with searchable encryption algorithms. Searchable encryption is a family of cryptographic protocols that facilitate private keyword searches directly on encrypted data. These protocols allow data owners to upload their encrypted data to the cloud, while retaining the ability to query over uploaded data. In this project, we focus on symmetric searchable encryption schemes, as well as apply an efficient searchable encryption scheme which supports multi-keyword searches to provide a privacy preserving keyword search framework for health data. Our framework applies a recent secure searchable encryption scheme and employs an inverted indexing structure in order to process queries in a privacy-preserving manner

Secure and practical computation on encrypted data

Because of the importance of computing on data with privacy protections, the cryptographic community has developed both theoretical and practical solutions to compute on encrypted data. On the one hand, theoretical schemes, such as fully homomorphic encryption and functional encryption, are secure but extremely inefficient. On the other hand, practical schemes, such as property-preserving encryption, gain efficiency by accepting significant reductions in security. In this thesis, we first study the security of popular property-preserving encryption schemes that are being used by companies such as Microsoft and Google. We show that such schemes are unacceptably insecure for key target applications such as electronic medical records. Second, we propose new models to compute on encrypted data and develop efficient constructions and systems. We propose a new cryptographic primitive called Blind Storage and show how it can be used to realize symmetric searchable encryption, which is much more secure than property-preserving encryption. Finally, we propose a new cryptographic model called Controlled Functional Encryption and develop two efficient schemes in this model

A Practical Framework for Storing and Searching Encrypted Data on Cloud Storage

Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.Comment: 146 Pages, Master's Thesis, 6 Chapters, 96 Figures, 11 Table

Pseudonymization and its Application to Cloud-based eHealth Systems

Responding to the security and privacy issues of information systems, we propose a novel pseudonym solution. This pseudonym solution has provable security to protect the identities of users by employing user-generated pseudonyms. It also provides an encryption scheme to protect the security of the users’ data stored in the public network. Moreover, the pseudonym solution also provides the authentication of pseudonyms without disclosing the users’ identity information. Thus the dependences on powerful trusted third parties and on the trustworthiness of system administrators may be appreciably alleviated. Electronic healthcare systems (eHealth systems), as one kind of everyday information system, with the ability to store and share patients’ health data efficiently, have to manage in-formation of an extremely personal nature. As a consequence of known cases of abuse and attacks, the security of the health data and the privacy of patients are a great concern for many people and thus becoming obstacles to the acceptance and spread of eHealth systems. In this thesis, we survey current eHealth systems in both research and practice, analyzing potential threats to the security and privacy. Cloud-based eHealth systems, in particular, enable applications with many new features in data storing and sharing. We analyze the new issues on security and privacy when cloud technology is introduced into eHealth systems. We demonstrate that our proposed pseudonym solution can be successfully applied to cloud-based eHealth systems. Firstly, we utilize the pseudonym scheme and encryption scheme for storing and retrieving the electronic health records (EHR) in the cloud. The identities of patients and the confidentiality of EHR contents are provably guaranteed by advanced cryptographic algorithms. Secondly, we utilize the pseudonym solution to protect the privacy of patients from the health insurance companies. Only necessary information about patients is disclosed to the health insurance companies, without interrupting the cur-rent normal business processes of health insurance. At last, based on the pseudonym solution, we propose a new procedure for the secondary use of the health data. The new procedure protects the privacy of patients properly and enables patients’ full control and clear consent over their health data to be secondarily used. A prototypical application of a cloud-based eHealth system implementing our proposed solution is presented in order to exhibit the practicability of the solution and to provide intuitive experiences. Some performance estimations of the proposed solution based on the implementation are also provided.Um gewisse Sicherheits- und Datenschutzdefizite heutiger Informationssysteme zu beheben, stellen wir eine neuartige Pseudonymisierungslösung vor, die benutzergenerierte Pseudonyme verwendet und die Identitäten der Pseudonyminhaber nachweisbar wirksam schützt. Sie beinhaltet neben der Pseudonymisierung auch ein Verschlüsselungsverfahren für den Schutz der Vertraulichkeit der Benutzerdaten, wenn diese öffentlich gespeichert werden. Weiterhin bietet sie ein Verfahren zur Authentisierung von Pseudonymen, das ohne die Offenbarung von Benutzeridentitäten auskommt. Dadurch können Abhängigkeiten von vertrauenswürdigen dritten Stellen (trusted third parties) oder von vertrauenswürdigen Systemadministratoren deutlich verringert werden. Elektronische Gesundheitssysteme (eHealth-Systeme) sind darauf ausgelegt, Patientendaten effizient zu speichern und bereitzustellen. Solche Daten haben ein extrem hohes Schutzbedürfnis, und bekannte Fälle von Angriffen auf die Vertraulichkeit der Daten durch Privilegienmissbrauch und externe Attacken haben dazu geführt, dass die Sorge um den Schutz von Gesundheitsdaten und Patientenidentitäten zu einem großen Hindernis für die Verbreitung und Akzeptanz von eHealth-Systemen geworden ist. In dieser Dissertation betrachten wir gegenwärtige eHealth-Systeme in Forschung und Praxis hinsichtlich möglicher Bedrohungen für Sicherheit und Vertraulichkeit der gespeicherten Daten. Besondere Beachtung finden cloudbasierte eHealth-Systeme, die Anwendungen mit neuartigen Konzepten zur Datenspeicherung und -bereitstellung ermöglichen. Wir analysieren Sicherheits- und Vertraulichkeitsproblematiken, die sich beim Einsatz von Cloud-Technologie in eHealth-Systemen ergeben. Wir zeigen, dass unsere Pseudonymisierungslösung erfolgreich auf cloudbasierte eHealth-Systeme angewendet werden kann. Dabei werden zunächst das Pseudonymisierungs- und das Verschlüsselungsverfahren bei der Speicherung und beim Abruf von elektronischen Gesundheitsdatensätzen (electronic health records, EHR) in der Cloud eingesetzt. Die Vertraulichkeit von Patientenidentitäten und EHR-Inhalten werden dabei durch den Einsatz moderner kryptografischer Algorithmen nachweisbar garantiert. Weiterhin setzen wir die Pseudonymisierungslösung zum Schutz der Privatsphäre der Patienten gegenüber Krankenversicherungsunternehmen ein. Letzteren werden lediglich genau diejenigen Patienteninformationen offenbart, die für den störungsfreien Ablauf ihrer Geschäftsprozesse nötig sind. Schließen schlagen wir eine neuartige Vorgehensweise für die Zweitverwertung der im eHealth-System gespeicherten Daten vor, die die Pseudonymisierungslösung verwendet. Diese Vorgehensweise bietet den Patienten angemessenen Schutz für ihre Privatsphäre und volle Kontrolle darüber, welche Daten für eine Zweitverwertung (z.B. für Forschungszwecke) freigegeben werden. Es wird ein prototypisches, cloudbasiertes eHealth-System vorgestellt, das die Pseudonymisierungslösung implementiert, um deren Praktikabilität zu demonstrieren und intuitive Erfahrungen zu vermitteln. Weiterhin werden, basierend auf der Implementierung, einige Abschätzungen der Performanz der Pseudonymisierungslösung angegeben

Selected Computing Research Papers Volume 2 June 2013

An Evaluation of Current Innovations for Solving Hard Disk Drive Vibration Problems (Isiaq Adeola) ........................................................................................................ 1 A Critical Evaluation of the Current User Interface Systems Used By the Blind and Visually Impaired (Amneet Ahluwalia) ................................................................................ 7 Current Research Aimed At Improving Bot Detection In Massive Multiplayer Online Games (Jamie Burnip) ........................................................................................................ 13 Evaluation Of Methods For Improving Network Security Against SIP Based DoS Attacks On VoIP Network Infrastructures (David Carney) ................................................ 21 An Evaluation of Current Database Encryption Security Research (Ohale Chidiebere) .... 29 A Critical Appreciation of Current SQL Injection Detection Methods (Lee David Glynn) .............................................................................................................. 37 An Analysis of Current Research into Music Piracy Prevention (Steven Hodgson) .......... 43 Real Time On-line Analytical Processing: Applicability Of Parallel Processing Techniques (Kushatha Kelebeng) ....................................................................................... 49 Evaluating Authentication And Authorisation Method Implementations To Create A More Secure System Within Cloud Computing Technologies (Josh Mallery) ................... 55 A Detailed Analysis Of Current Computing Research Aimed At Improving Facial Recognition Systems (Gary Adam Morrissey) ................................................................... 61 A Critical Analysis Of Current Research Into Stock Market Forecasting Using Artificial Neural Networks (Chris Olsen) ........................................................................... 69 Evaluation of User Authentication Schemes (Sukhdev Singh) .......................................... 77 An Evaluation of Biometric Security Methods for Use on Mobile Devices (Joe van de Bilt) .................................................................................................................. 8

Smart and Secure Augmented Reality for Assisted Living

Augmented reality (AR) is one of the biggest technology trends which enables people to see the real-life surrounding environment with a layer of virtual information overlaid on it. Assistive devices use this match of information to help people better understand the environment and consequently be more efficient. Specially, AR has been extremely useful in the area of Ambient Assisted Living (AAL). AR-based AAL solutions are designed to support people in maintaining their autonomy and compensate for slight physical and mental restrictions by instructing them on everyday tasks. The discovery of visual attention for assistive aims is a big challenge since in dynamic cluttered environments objects are constantly overlapped and partial object occlusion is also frequent. Current solutions use egocentric object recognition techniques. However, the lack of accuracy affects the system's ability to predict users’ needs and consequently provide them with the proper support. Another issue is the manner that sensitive data is treated. This highly private information is crucial for improving the quality of healthcare services. However, current blockchain approaches are used only as a permission management system, while the data is still stored locally. As a result, there is a potential risk of security breaches. Privacy risk in the blockchain domain is also a concern. As major investigation tackles privacy issues based on off-chain approaches, there is a lack of effective solutions for providing on-chain data privacy. Finally, the Blockchain size has been shown to be a limiting factor even for chains that store simple transactional data, much less the massive blocks that would be required for storing medical imaging studies. To tackle the aforementioned major issues, this research proposes a framework to provide a smarter and more secure AR-based solution for AAL. Firstly, a combination of head-worn eye-trackers cameras with egocentric video is designed to improve the accuracy of visual attention object recognition in free-living settings. A heuristic function is designed to generate a probability estimation of visual attention over objects within an egocentric video. Secondly, a novel methodology for the storage of large sensitive AR-based AAL data is introduced in a decentralized fashion. By leveraging the power of the IPFS (InterPlanetary File System) protocol to tackle the lack of storage issue in the Blockchain. Meanwhile, a blockchain solution on the Secret Network blockchain is developed to tackle the existent lack of privacy on smart contracts, which provides data privacy at both transactional and computational levels. In addition, is included a new off-chain solution encapsulates a governing body for permission management purposes to solve the problem of the lost or eventual theft of private keys. Based on the research findings, that visual attention-object detection approach is applicable to cluttered environments which presents a transcend performance compared to the current methods. This study also produced an egocentric indoor dataset annotated with human fixation during natural exploration in a cluttered environment. Comparing to previous works, this dataset is more realistic because it was recorded in real settings with variations in terms of objects overlapping regions and object sizes. With respect to the novel decentralized storage methodology, results indicate that sensitive data can be stored and queried efficiently using the Secret Network blockchain. The proposed approach achieves both computational and transactional privacy with significantly less cost. Additionally, this approach mitigates the risk of permanent loss of access to the patient on-chain data records. The proposed framework can be applied as an assistive technology in a wide range of sectors that requires AR-based solution with high-precision visual-attention object detection, efficient data access, high-integrity data storage and full data privacy and security