Integrity and Privacy of Large Data

There has been considerable recent interest in "cloud storage" wherein a user asks a server to store a large file. One issue is whether the user can verify that the server is actually storing the file, and typically a challenge-response protocol is employed to convince the user that the file is indeed being stored correctly. The security of these schemes is phrased in terms of an extractor which will recover the file given any ``proving algorithm'' that has a sufficiently high success probability. This forms the basis of proof-of-retrievability (PoR) and proof-of-data-possession (PDP) systems. The contributions of this thesis in secure cloud storage are as below. 1. We provide a general analytical framework for various known PoR schemes that yields exact reductions that precisely quantify conditions for extraction to succeed as a function of the success probability of a proving algorithm. We apply this analysis to several archetypal schemes. In addition, we provide a new methodology for the analysis of keyed PoR schemes in an unconditionally secure setting, and use it to prove the security of a modified version of a scheme due to Shacham and Waters (ASIACRYPT, 2009) under a slightly restricted attack model, thus providing the first example of a keyed PoR scheme with unconditional security. We also show how classical statistical techniques can be used to evaluate whether the responses of the prover on the storage are accurate enough to permit successful extraction. Finally, we prove a new lower bound on the storage and communication complexity of PoR schemes. 2. We propose a new type of scheme that we term a proof-of-data-observability scheme. Our definition tries to capture the stronger requirement that the server must have an actual copy of M in its memory space while it executes the challenge-response protocol. We give some examples of schemes that satisfy this new security definition. As well, we analyze the efficiency and security of the protocols we present, and we prove some necessary conditions for the existence of these kinds of protocols. 3. We study secure storage on multiple servers. Our contribution in multiple-server PoR systems is twofold. We formalize security definitions for two possible scenarios: (i) when a threshold of servers succeed with high enough probability (worst-case) and (ii) when the average of the success probability of all the servers is above a threshold (average-case). Using coding theory, we show instances of protocols that are secure both in the average-case and the worst-case scenarios

A Hybrid Multi-user Cloud Access Control based Block Chain Framework for Privacy Preserving Distributed Databases

Most of the traditional medical applications are insecure and difficult to compute the data integrity with variable hash size. Traditional medical data security systems are insecure and it depend on static parameters for data security. Also, distributed based cloud storage systems are independent of integrity computational and data security due to unstructured data and computational memory. As the size of the data and its dimensions are increasing in the public and private cloud servers, it is difficult to provide the machine learning based privacy preserving in cloud computing environment. Block-chain technology plays a vital role for large cloud databases. Most of the conventional block-chain frameworks are based on the existing integrity and confidentiality models. Also, these models are based on the data size and file format. In this model, a novel integrity verification and encryption framework is designed and implemented in cloud environment.  In order to overcome these problems in the cloud computing environment, a hybrid integrity and security-based block-chain framework is designed and implemented on the large distributed databases. In this framework,a novel decision tree classifier is used along with non-linear mathematical hash algorithm and advanced attribute-based encryption models are used to improve the privacy of multiple users on the large cloud datasets. Experimental results proved that the proposed advanced privacy preserving based block-chain technology has better efficiency than the traditional block-chain based privacy preserving systems on large distributed databases

Applying contextual integrity to the study of social network sites

Social network sites (SNSs) have become very popular, with more than 1.39 billion people using Facebook alone. The ability to share large amounts of personal information with these services, such as location traces, photos, and messages, has raised a number of privacy concerns. The popularity of these services has enabled new research directions, allowing researchers to collect large amounts of data from SNSs to gain insight into how people share information, and to identify and resolve issues with such services. There are challenges to conducting such research responsibly, ensuring studies are ethical and protect the privacy of participants, while ensuring research outputs are sustainable and can be reproduced in the future. These challenges motivate the application of a theoretical framework that can be used to understand, identify, and mitigate the privacy impacts of emerging SNSs, and the conduct of ethical SNS studies. In this thesis, we apply Nissenbaum's model of contextual integrity to the study of SNSs. We develop an architecture for conducting privacy-preserving and reproducible SNS studies that upholds the contextual integrity of participants. We apply the architecture to the study of informed consent to show that contextual integrity can be leveraged to improve the acquisition of consent in such studies. We then use contextual integrity to diagnose potential privacy violations in an emerging form of SNS

Supporting the clinical trial recruitment process through the grid

Patient recruitment for clinical trials and studies is a large-scale task. To test a given drug for example, it is desirable that as large a pool of suitable candidates is used as possible to support reliable assessment of often moderate effects of the drugs. To make such a recruitment campaign successful, it is necessary to efficiently target the petitioning of these potential subjects. Because of the necessarily large numbers involved in such campaigns, this is a problem that naturally lends itself to the paradigm of Grid technology. However the accumulation and linkage of data sets across clinical domain boundaries poses challenges due to the sensitivity of the data involved that are atypical of other Grid domains. This includes handling the privacy and integrity of data, and importantly the process by which data can be collected and used, and ensuring for example that patient involvement and consent is dealt with appropriately throughout the clinical trials process. This paper describes a Grid infrastructure developed as part of the MRC funded VOTES project (Virtual Organisations for Trials and Epidemiological Studies) at the National e-Science Centre in Glasgow that supports these processes and the different security requirements specific to this domain

Aggregatable Certificateless Designated Verifier Signature

In recent years, the Internet of Things (IoT) devices have become increasingly deployed in many industries and generated a large amount of data that needs to be processed in a timely and efficient manner. Using aggregate signatures, it provides a secure and efficient way to handle large numbers of digital signatures with the same message. Recently, the privacy issue has been concerned about the topic of data sharing on the cloud. To provide the integrity, authenticity, authority, and privacy on the data sharing in the cloud storage, the notion of an aggregatable certificateless designated verifier signature scheme (ACLDVS) was proposed. ACLDVS also is a perfect tool to enable efficient privacy-preserving authentication systems for IoT and or the vehicular ad hoc networks (VANET). Our concrete scheme was proved to be secured underling of the Computational Diffie-Hellman assumption. Compared to other related schemes, our scheme is efficient, and the signature size is considerably short

A Novel Approach for Efficient User Revocation with Maintaining Shared Data Integrity on Cloud

Cloud computing is the biggest innovation in computing world. It provides great facilities of data sharing and data storing to its users. Here a main risk occurs as data security in aspects of data integrity, data privacy and data access by unauthorized users. TTA (Trusted Third Party) is used by cloud service providers to ensure data security and privacy. In cloud, data modification and data sharing among the group of users is very simple task .To maintain integrity of the shared data,group members needs to compute signatures on all shared data which are available in blocks. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. User revocation is one of the biggest security issue during data sharing. After user revocation, shared data signed by revoked user, needs to re-sign by existing user.This task is very inefficacious due to the large size of shared data needs to download before re-signing it. This paper is a detail description of cloud public auditor which is used for the maintaining integrity of shared data with efficient user revocation in the cloud. This mechanism uses concept of proxy re-signatures which allows the cloud to re-sign blocks on behalf of existing users during user revocation, so there is no need of data downloading. It also performs batch monitoring to verify multiple tasks simultaneously. DOI: 10.17762/ijritcc2321-8169.150612

Responding to Some Challenges Posed by the Re-identification of Anonymized Personal Data

In this paper, we examine a cluster of ethical controversies generated by the re-identification of anonymized personal data in the context of big data analytics, with particular attention to the implications for personal privacy. Our paper is organized into two main parts. Part One examines some ethical problems involving re-identification of personally identifiable information (PII) in large data sets. Part Two begins with a brief description of Moor and Weckert’s Dynamic Ethics (DE) and Nissenbaum’s Contextual Integrity (CI) Frameworks. We then investigate whether these frameworks, used together, can provide us with a more robust scheme for analyzing privacy concerns that arise in the re-identification process (as well as within the larger context of big data analytics). This paper does not specifically address re-identification-related privacy concerns that arise in the context of the European Union’s General Data Protection Regulation (GDPR). Instead, we examine those issues in a separate work

Privacy-Enhanced Dependable and Searchable Storage in a Cloud-of-Clouds

In this dissertation we will propose a solution for a trustable and privacy-enhanced storage architecture based on a multi-cloud approach. The solution provides the necessary support for multi modal on-line searching operation on data that is always maintained encrypted on used cloud-services. We implemented a system prototype, conducting an experimental evaluation. Our results show that the proposal offers security and privacy guarantees, and provides efficient information retrieval capabilities without sacrificing precision and recall properties on the supported search operations. There is a constant increase in the demand of cloud services, particularly cloud-based storage services. These services are currently used by different applications as outsourced storage services, with some interesting advantages. Most personal and mobile applications also offer the user the choice to use the cloud to store their data, transparently and sometimes without entire user awareness and privacy-conditions, to overcome local storage limitations. Companies might also find that it is cheaper to outsource databases and keyvalue stores, instead of relying on storage solutions in private data-centers. This raises the concern about data privacy guarantees and data leakage danger. A cloud system administrator can easily access unprotected data and she/he could also forge, modify or delete data, violating privacy, integrity, availability and authenticity conditions. A possible solution to solve those problems would be to encrypt and add authenticity and integrity proofs in all data, before being sent to the cloud, and decrypting and verifying authenticity or integrity on data downloads. However this solution can be used only for backup purposes or when big data is not involved, and might not be very practical for online searching requirements over large amounts of cloud stored data that must be searched, accessed and retrieved in a dynamic way. Those solutions also impose high-latency and high amount of cloud inbound/outbound traffic, increasing the operational costs. Moreover, in the case of mobile or embedded devices, the power, computation and communication constraints cannot be ignored, since indexing, encrypting/decrypting and signing/verifying all data will be computationally expensive. To overcome the previous drawbacks, in this dissertation we propose a solution for a trustable and privacy-enhanced storage architecture based on a multi-cloud approach, providing privacy-enhanced, dependable and searchable support. Our solution provides the necessary support for dependable cloud storage and multi modal on-line searching operations over always-encrypted data in a cloud-of-clouds. We implemented a system prototype, conducting an experimental evaluation of the proposed solution, involving the use of conventional storage clouds, as well as, a high-speed in-memory cloud-storage backend. Our results show that the proposal offers the required dependability properties and privacy guarantees, providing efficient information retrieval capabilities without sacrificing precision and recall properties in the supported indexing and search operations