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Recently, Internet‐of‐Things (IoT) based applications have shifted from centralized infrastructures to decentralized ecosystems, owing to user data's security and privacy limitations. The shift has opened new doors for intruders to launch distributed attacks in diverse IoT scenarios that jeopardize the application environments. Moreover, as heterogeneous and autonomous networks communicate, the attacks intensify, which justifies the requirement of trust as a key policy. Recently, blockchain‐based IoT solutions have been proposed that address trust limitations by maintaining data consistency, immutability, and chronology in IoT environments. However, IoT ecosystems are resource‐constrained and have low bandwidth and finite computing power of sensor nodes. Thus, the inclusion of blockchain requires an effective policy design regarding consensus and smart contract environments in heterogeneous IoT applications. Recent studies have presented blockchain as a potential solution in IoT, but an effective view of consensus and smart contract design to meet the end application requirements is an open problem. Motivated by the same, the survey presents the integration of suitable low‐powered consensus protocols and smart contract design to assess and validate the blockchain‐IoT ecosystems. We present blockchain‐IoT's emerging communication and security aspects with performance issues of consensus protocols, interoperability, and implementation platforms. A case study of a smart contract‐based blockchain‐driven ecosystem is presented with a comparative analysis of mining cost and latency, which shows its suitability in real‐world setups. We also highlight attacks on blockchain IoT, open issues, potential findings, and future directions. The survey intends to drive novel solutions for future consensus and safe, smart contract designs to support applicative IoT ecosystems. [ABSTRACT FROM AUTHOR]

Summary: Location‐based services (LBS) has become an intrinsic part of our everyday life. However, the flexibility and convenience provided by LBS are at the cost of user privacy since untrusted LBS server can leak private information of users. To overcome the privacy issues observed in LBS, a novel dummy‐generation based privacy preservation technique is proposed in this article. The proposed dummy‐generation technique is a circle‐based technique which generates dummy locations in the circle area and is effective against center‐of‐anonymized spatial region attack, map‐matching attack, and location‐homogeneity attack. Additionally, an edge computing enabled framework is proposed, which can be used in the IoT environment. The edge computing enabled framework helps in handling the resource poverty issues of the service requesting devices and provides the low latency solution. The security analysis of our proposed dummy‐generation technique reflects that the proposed technique is resilient to specific attacks for different adversary attack models. The results obtained through simulations suggest that our technique performs better than the pre‐existing techniques. [ABSTRACT FROM AUTHOR]

In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k -anonymity, l -diversity, and t -closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k -anonymity and l -diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l- Diversity (ImSLD) approach which is the extension of Improved Scalable k -Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k -anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class. [ABSTRACT FROM AUTHOR]

The prevalent usage of Location Based Services; where getting any informational service is solely based on the user's current location, have raised an extreme concern over location privacy of the user. The privacy concern becomes paramount when the location tagged data publication like government health care data, district crime record data and the like, are reverse engineered by an adversary to pinpoint the real user against the location given in the specific tuple of the record. Address information is typically considered as a confidential element of the published record and any linkages of this piece of information with publicly available quasi identifier is enough to reveal a lot about a user (which is not apparent otherwise) or hamper the social reputation of the user considering the extreme case. Various geographical masking techniques have been presented and discussed at length in the literature, however, no scheme is able to dispense privacy providing absolute usefulness of the published data. This work is a research attempt to recognize the current state-of-the-art in geographical masking, supportive analysis of the existing masking technique, and come up with a robust solution that serves the purpose of location privacy without making published data worthless. The suggested solution is well suited for geocoded, static, discrete point published data. [ABSTRACT FROM AUTHOR]

Various sources and sophisticated tools are used to gather and process the comparatively large volume of data or big data that sometimes leads to privacy disclosure (at broader or finer level) for the data owner. Privacy preserving data publishing approaches such as k-anonymity, l-diversity, and t-closeness are very well used to de-identify data, however, chances of re-identification of attributes always exist as data is collected from multiple sources such as public web, social media, Internet whereabouts, and sensors that are highly prone to data linkages. In literature, k-anonymity stands out amongst the most popular mainstream data anonymization approaches that can also be used for large sized data. However, applying k-anonymization for variety of data (especially unstructured data) is difficult in the traditional way, due to the fact that it requires the given data to be classified into the personal data, the quasi identifiers, and the sensitive data. We identify existing approaches from the literature of Natural Language Processing(NLP) to convert the unstructured data to structured form in order to apply k-anonymization over the generated structured records. We adopt a two phase Conditional Random Field (CRF) based Named Entity Recognition (NER) approach to represent unstructured data into the structured form. Further, we propose an Improved Scalable k-Anonymization (ImSKA) to anonymize the well represented unstructured data that achieves privacy preserving unstructured big data publishing. We compare both of the propose approaches namely NER and ImSKA with existing approaches and the results show that our proposed solutions outperform the existing approaches in terms of F1 score and Normalized Cardinality Penalty (NCP), respectively. Since, NER approaches are widely used for bio-medical datasets, we have also used a well-known Bio-NER dataset called GENIA corpus for measuring the performance. [ABSTRACT FROM AUTHOR]

Abstract The trends of data mining in the healthcare is increased due to the digitization of healthcare with electronic health record (EHR) systems. This generates a huge amount of data on daily basis. Data mining with the healthcare data has given the new direction to medical research for early detection of diseases and improving patient care. Many data mining applications require the integration of data from the different sources. For example, the integration of outpatient medical records and health examination data helps to identify the correlation between abnormal test result and disease. The result of association rule mining on this integrated data helps to build the knowledge center for disease prevention, which facilitate the healthcare provider in follow up treatment and prevention. The integration of data requires the sharing of sensitive information about the patients. Disclosing the sensitive information violates the privacy of patients. In this paper, we tackle the problem of privacy preserving association rule mining in vertically partition healthcare data. Furthermore, we analyze the proposed approach in terms of privacy preservation, communication and computation cost. [ABSTRACT FROM AUTHOR]

The prevalent usage of location based services, where getting any service is solely based on the user's current location, has raised an extreme concern over location privacy of the user. Generalized approaches dealing with location privacy, referred to as cloaking and obfuscation, are mainly based on a trusted third party, in which all the data remain available at a central server and thus complete knowledge of the query exists at the central node. This is the major limitation of such approaches; on the other hand, in trusted third-party-free framework clients collaborate with each other and freely communicate with the service provider without any third-party involvement. Measuring and evaluating trust among peers is a crucial aspect in trusted third-party-free framework. This paper exploits the merits and mitigating the shortcomings of both of these approaches. We propose a hybrid solution, HYB, to achieve location privacy for the mobile users who use location services frequently. The proposed HYB scheme is based on the collaborative preprocessing of location data and utilizes the benefits of homomorphic encryption technique. Location privacy is achieved at two levels, namely, at the proximity level and at distant level. The proposed HYB solution preserves the user's location privacy effectively under specific, pull-based, sporadic query scenario. [ABSTRACT FROM AUTHOR]

Big data analytics has created opportunities for researchers to process huge amount of data but created a big threat to privacy of individual. Data processed by big data analytics platforms may have personal information which need to be taken care of when deriving some useful results for research. Existing privacy preserving techniques like, anonymization requires having dataset divided in the set of attributes like, sensitive attributes, quasi identifiers, and non-sensitive attributes. With the structured data it may possible to have such a distribution but in unstructured data it is very difficult to identify sensitive attribute and quasi identifiers. [ABSTRACT FROM AUTHOR]