A Novel approach for Privacy Preserving in Video using Extended Euclidean algorithm Based on Chinese remainder theorem

The development in the modern technology paved a path in the utilization of surveillance cameras in streets, offices and other areas but this significantly leads a threat to the privacy of visitors, passengers or employees, leakage of information etc.. To overcome this threat, privacy and security needs to be incorporated in the practical surveillance system. It secures the video information which is resided in various video file types. In this process we used an efficient framework to preserve the privacy while distributing secret among ‘N’ number of parties. In this paper we analyzed various techniques of Chinese Remainder Theorem

Distributed authorization in loosely coupled data federation

The underlying data model of many integrated information systems is a collection of inter-operable and autonomous database systems, namely, a loosely coupled data federation. A challenging security issue in designing such a data federation is to ensure the integrity and confidentiality of data stored in remote databases through distributed authorization of users. Existing solutions in centralized databases are not directly applicable here due to the lack of a centralized authority, and most solutions designed for outsourced databases cannot easily support frequent updates essential to a data federation. In this thesis, we provide a solution in three steps. First, we devise an architecture to support fully distributed, fine-grained, and data-dependent authorization in loosely coupled data federations. For this purpose, we adapt the integrity-lock architecture originally designed for multilevel secure databases to data federations. Second, we propose an integrity mechanism to detect, localize, and verify updates of data stored in remote databases while reducing communication overhead and limiting the impact of unauthorized updates. We realize the mechanism as a three-stage procedure based on a grid of Merkle Hash Trees built on relational tables. Third, we present a confidentiality mechanism to control remote users' accesses to sensitive data while allowing authorization policies to be frequently updated. We achieve this objective through a new over-encryption scheme based on secret sharing. Finally, we evaluate the proposed architecture and mechanisms through experiments

Society-oriented cryptographic techniques for information protection

Groups play an important role in our modern world. They are more reliable and more trustworthy than individuals. This is the reason why, in an organisation, crucial decisions are left to a group of people rather than to an individual. Cryptography supports group activity by offering a wide range of cryptographic operations which can only be successfully executed if a well-defined group of people agrees to co-operate. This thesis looks at two fundamental cryptographic tools that are useful for the management of secret information. The first part looks in detail at secret sharing schemes. The second part focuses on society-oriented cryptographic systems, which are the application of secret sharing schemes in cryptography. The outline of thesis is as follows

ReTRACe: Revocable and Traceable Blockchain Rewrites using Attribute-based Cryptosystems

In this paper, we study efficient and authorized rewriting of transactions already written to a blockchain. Mutable transactions will make a fraction of all blockchain transactions, but will be a necessity to meet the needs of privacy regulations, such as the General Data Protection Regulation (GDPR). The state-of-the-art rewriting approaches have several shortcomings, such as lack of user anonymity, inefficiency, and absence of revocation mechanisms. We present ReTRACe, an efficient framework for blockchain rewrites. ReTRACe is designed by composing a novel revocable chameleon hash with ephemeral trapdoor scheme, a novel revocable fast attribute based encryption scheme, and a dynamic group signature scheme. We discuss ReTRACe, and its constituent primitives in detail, along with their security analyses, and present experimental results to demonstrate the scalability of ReTRACe

Privacy engineering for social networks

In this dissertation, I enumerate several privacy problems in online social networks (OSNs) and describe a system called Footlights that addresses them. Footlights is a platform for distributed social applications that allows users to control the sharing of private information. It is designed to compete with the performance of today's centralised OSNs, but it does not trust centralised infrastructure to enforce security properties. Based on several socio-technical scenarios, I extract concrete technical problems to be solved and show how the existing research literature does not solve them. Addressing these problems fully would fundamentally change users' interactions with OSNs, providing real control over online sharing. I also demonstrate that today's OSNs do not provide this control: both user data and the social graph are vulnerable to practical privacy attacks. Footlights' storage substrate provides private, scalable, sharable storage using untrusted servers. Under realistic assumptions, the direct cost of operating this storage system is less than one US dollar per user-year. It is the foundation for a practical shared filesystem, a perfectly unobservable communications channel and a distributed application platform. The Footlights application platform allows third-party developers to write social applications without direct access to users' private data. Applications run in a confined environment with a private-by-default security model: applications can only access user information with explicit user consent. I demonstrate that practical applications can be written on this platform. The security of Footlights user data is based on public-key cryptography, but users are able to log in to the system without carrying a private key on a hardware token. Instead, users authenticate to a set of authentication agents using a weak secret such as a user-chosen password or randomly-assigned 4-digit number. The protocol is designed to be secure even in the face of malicious authentication agents.This work was supported by the Rothermere Foundation and the Natural Sciences and Engineering Research Council of Canada (NSERC)

Secret sharing using artificial neural network

Secret sharing is a fundamental notion for secure cryptographic design. In a secret sharing scheme, a set of participants shares a secret among them such that only pre-specified subsets of these shares can get together to recover the secret. This dissertation introduces a neural network approach to solve the problem of secret sharing for any given access structure. Other approaches have been used to solve this problem. However, the yet known approaches result in exponential increase in the amount of data that every participant need to keep. This amount is measured by the secret sharing scheme information rate. This work is intended to solve the problem with better information rate

Secret Sharing with Public Reconstruction

All known constructions of information theoretic t-out-of-n secret sharing schemes require secure, private communication channels among the parties for the reconstruction of the secret. In this work we investigate the cost of performing the reconstruction over public communication channels. A naive implementation of this task distributes 2n \Gamma 2 one times pads to each party. This results in shares whose size is 2n \Gamma 1 times the secret size. We present three implementations of such schemes that are substantially more efficient: ffl A scheme enabling multiple reconstructions of the secret by different subsets of parties, with factor O(n=t) increase in the shares' size. ffl A one-time scheme, enabling a single reconstruction of the secret, with O(log(n=t)) increase in the shares' size. ffl A one-time scheme, enabling a single reconstruction by a set of size exactly t, with factor O(1) increase in the shares' size. We prove that the first implementation is optimal (up to co..