Data Mining - Concepts and Techniques.

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Information Security in Big Data: Privacy and Data Mining

Internet of Things (IoT) is one of the most buzzing and discussed topic in research field today. Some of the researchers are also looking future of the world in this technology. Since then significant research and development have taken place on IoT, however various vulnerabilities are observed which shall keep IoT as a technology in danger. As a result, there are so many attacks on IoT have been invented before actual commercial implementation of it. The present study discusses about various IoT attacks happening, classify them, its countermeasures and finding the most prominent attacks in IoT. A state of the art survey about the various attacks have been presented and compared including their efficiency and damage level in IoT.

Security attacks in IoT: A survey

We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions.

Computation Offloading Toward Edge Computing

Privacy preserving data mining (PPDM) is a novel research direction to preserve privacy for sensitive knowledge from disclosure. Many of the researchers in this area have recently made effort to preserve privacy for sensitive association rules in statistical database. In this paper, we propose a heuristic algorithm named DSRRC (Decrease Support of R.H.S. item of Rule Clusters), which provides privacy for sensitive rules at certain level while ensuring data quality. Proposed algorithm clusters the sensitive association rules based on certain criteria and hides as many as possible rules at a time by modifying fewer transactions. Because of less modification in database it helps maintaining data quality.

Maintaining privacy and data quality in privacy preserving association rule mining

Mobile gadgets today are swaggering computing potential and memory at par or at times even higher to that found in desktop personal computers. A wireless interconnection has turned out to be considerably more readily accessible these days. As individuals are growing mobile with regard to the fast lifestyle and working pattern, a new, smarter system came into existence that is termed as ‘Location Based Service’ (LBS). Such a system amalgamates the location data of a user with smart applications to deliver demanded services. Although LBS provide major openings for a large variety of markets and remarkable convenience to the end user, it also presents subtle privacy attack to user’s location information. Threat to the privacy sneaks into the system due to the prerequisite of sending user’s current location to the LBS provider to attain related services. Since the volume of data gathered from dynamic or stationary mobile users using LBS can be high, it is vital to outline the frameworks and systems in a manner that is secure and keep the location information private. In this paper, we perform an exploratory survey about the various techniques that have been suggested by many researchers based on centralized and distributed approaches, to preserve location privacy of the user. A large portion of these techniques has a trade-off between privacy, efficiency, applicability and quality of service. This paper details and analyses the various existing techniques for preserving location privacy of the participating user in LBS.

An Exploration to Location Based Service and Its Privacy Preserving Techniques: A Survey

Blockchain technology started as the backbone for cryptocurriencies and it has emerged as one of the most interesting technologies of the last decade. It is a new paradigm able to modify the way how industries transact. Today, the industries’ concern is about their ability to handle a high volume of data transactions per second while preserving both decentralization and security. Both decentralization and security are guaranteed by the mathematical strength of cryptographic primitives. There are two main approaches to achieve consensus: the Proof-of-Work based blockchains—PoW—and the Proof-of-Stake—PoS. Both of them come with some pros and drawbacks, but both rely on cryptography. In this survey, we present a review of the main consensus procedures, including the new consensus proposed by Algorand: Pure Proof-of-Stake—Pure PoS. In this article, we provide a framework to compare the performances of PoW, PoS and the Pure PoS, based on throughput and scalability.

A Survey on Blockchain Consensus with a Performance Comparison of PoW, PoS and Pure PoS

In this paper, we propose a heuristic based algorithm named MDSRRC (Modified Decrease Support of R.H.S. item of Rule Clusters) to hide the sensitive association rules with multiple items in consequent (R.H.S) and antecedent (L.H.S). This algorithm overcomes the limitation of existing rule hiding algorithm DSRRC. Proposed algorithm selects the items and transactions based on certain criteria which modify transactions to hide the sensitive information. Experimental result shows that proposed algorithm is highly efficient and maintains database quality.

Hiding sensitive association rules to maintain privacy and data quality in database

The widespread usage of location based services (LBS); where obtaining any informational service is entirely based upon the user's current location, have raised a significant concern about location privacy of the user. For the queries like ‘where is my closest ATM?’, ‘where is my nearest hospital’, or any route assistance in general, it is essential to submit the user's actual location to avail the demanded services. Similar communication holds true for a location based vehicular transportation system. Cloaking and ob-fuscation are the two generalized approaches to deal with location privacy preservation in LBS. These approaches are mainly based on a trusted third party (TTP) and exploits the well established K-anonymity principle in order to make the query issuer indistinguishable with other K − 1 more users. In such approaches all the data (mainly location coordinates and queries) becomes available at the central server, thus complete knowledge of the query (including user id) exists at central node. This is the major limitation of TTP based architecture and makes such frameworks susceptible to different privacy attacks. This paper is a research attempt to extend the realm of collaborative communication among peers belonging to a mobile user group in a decentralized or trusted third party free architecture. We propose a collaborative P2P communication model; called CAST, that employs the series of trust among peers and peers use their cached mobile data to collaborate with each other in order to get the results locally. The scheme provides results locally with low latency and works efficiently when the peers share common inclinations (or data value). The proposed algorithm preserves user's privacy and performs effectively under pull-based sporadic query scenario.

Udai Pratap Rao

Papers

Maintaining privacy and data quality in privacy preserving association rule mining

An Exploration to Location Based Service and Its Privacy Preserving Techniques: A Survey

A Survey on Blockchain Consensus with a Performance Comparison of PoW, PoS and Pure PoS

Hiding sensitive association rules to maintain privacy and data quality in database

Achieving location privacy through CAST in location based services