Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices

In this paper a homomorphic privacy preserving association rule mining algorithm is proposed which can be deployed in resource constrained devices (RCD). Privacy preserved exchange of counts of itemsets among distributed mining sites is a vital part in association rule mining process. Existing cryptography based privacy preserving solutions consume lot of computation due to complex mathematical equations involved. Therefore less computation involved privacy solutions are extremely necessary to deploy mining applications in RCD. In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets encrypted by all mining sites without revealing individual values. The proposed algorithm is built on with a well known communication efficient association rule mining algorithm named count distribution (CD). Security proofs along with performance analysis and comparison show the well acceptability and effectiveness of the proposed algorithm. Efficient and straightforward privacy model and satisfactory performance of the protocol promote itself among one of the initiatives in deploying data mining application in RCD.Comment: IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

A survey of state-of-the-art methods for securing medical databases

This review article presents a survey of recent work devoted to advanced state-of-the-art methods for securing of medical databases. We concentrate on three main directions, which have received attention recently: attribute-based encryption for enabling secure access to confidential medical databases distributed among several data centers; homomorphic encryption for providing answers to confidential queries in a secure manner; and privacy-preserving data mining used to analyze data stored in medical databases for verifying hypotheses and discovering trends. Only the most recent and significant work has been included

Privacy Enhancing Technologies for solving the privacy-personalization paradox : taxonomy and survey

Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users’ physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users’ privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent - economical and ethical - purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category. Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions

Blockchain-enabled cybersecurity provision for scalable heterogeneous network: A comprehensive survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance, transportation, healthcare, education, and supply chain management. Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges. However, the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes. There is the biggest challenge of data integrity and scalability, including significant computing complexity and inapplicable latency on regional network diversity, operating system diversity, bandwidth diversity, node diversity, etc., for decision-making of data transactions across blockchain-based heterogeneous networks. Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems. To address these issues, today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain. The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network. This paper proposes a full-fledged taxonomy to identify the main obstacles, research gaps, future research directions, effective solutions, and most relevant blockchain-enabled cybersecurity systems. In addition, Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper to meet the goal of maintaining optimal performance data transactions among organizations. Overall, this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network

Provable Security for Cryptocurrencies

The past several years have seen the surprising and rapid rise of Bitcoin and other “cryptocurrencies.” These are decentralized peer-to-peer networks that allow users to transmit money, tocompose financial instruments, and to enforce contracts between mutually distrusting peers, andthat show great promise as a foundation for financial infrastructure that is more robust, efficientand equitable than ours today. However, it is difficult to reason about the security of cryptocurrencies. Bitcoin is a complex system, comprising many intricate and subtly-interacting protocol layers. At each layer it features design innovations that (prior to our work) have not undergone any rigorous analysis. Compounding the challenge, Bitcoin is but one of hundreds of competing cryptocurrencies in an ecosystem that is constantly evolving. The goal of this thesis is to formally reason about the security of cryptocurrencies, reining in their complexity, and providing well-defined and justified statements of their guarantees. We provide a formal specification and construction for each layer of an abstract cryptocurrency protocol, and prove that our constructions satisfy their specifications. The contributions of this thesis are centered around two new abstractions: “scratch-off puzzles,” and the “blockchain functionality” model. Scratch-off puzzles are a generalization of the Bitcoin “mining” algorithm, its most iconic and novel design feature. We show how to provide secure upgrades to a cryptocurrency by instantiating the protocol with alternative puzzle schemes. We construct secure puzzles that address important and well-known challenges facing Bitcoin today, including wasted energy and dangerous coalitions. The blockchain functionality is a general-purpose model of a cryptocurrency rooted in the “Universal Composability” cryptography theory. We use this model to express a wide range of applications, including transparent “smart contracts” (like those featured in Bitcoin and Ethereum), and also privacy-preserving applications like sealed-bid auctions. We also construct a new protocol compiler, called Hawk, which translates user-provided specifications into privacy-preserving protocols based on zero-knowledge proofs

Scaling Distributed Ledgers and Privacy-Preserving Applications

This thesis proposes techniques aiming to make blockchain technologies and smart contract platforms practical by improving their scalability, latency, and privacy. This thesis starts by presenting the design and implementation of Chainspace, a distributed ledger that supports user defined smart contracts and execute user-supplied transactions on their objects. The correct execution of smart contract transactions is publicly verifiable. Chainspace is scalable by sharding state; it is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT). This thesis also introduces a family of replay attacks against sharded distributed ledgers targeting cross-shard consensus protocols; they allow an attacker, with network access only, to double-spend resources with minimal efforts. We then build Byzcuit, a new cross-shard consensus protocol that is immune to those attacks and that is tailored to run at the heart of Chainspace. Next, we propose FastPay, a high-integrity settlement system for pre-funded payments that can be used as a financial side-infrastructure for Chainspace to support low-latency retail payments. This settlement system is based on Byzantine Consistent Broadcast as its core primitive, foregoing the expenses of full atomic commit channels (consensus). The resulting system has extremely low-latency for both confirmation and payment finality. Finally, this thesis proposes Coconut, a selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. It ensures authenticity and availability even when a subset of credential issuing authorities are malicious or offline, and natively integrates with Chainspace to enable a number of scalable privacy-preserving applications