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Hot data identification in flash memory is of great interest because it significantly affects the garbage collection and wear-leveling performance. Presently, certain hot and cold data classification schemes based on Bloom filters (BFs) have been proposed. Although BFs are efficient in most cases, there is a significant trade-off between false positive rates, which are the result of hash value collisions and memory utilization. In this paper, we suggest a better data categorization mechanism that is based on a hashing technique called Fingerprinting by Random Polynomials with the aim of reducing false positive rates and achieving lower memory consumption compared to the BF-based schemes. We also introduce a new methodology for classifying write requests by linking the definition of hot and cold write requests to the flash memory software layer, the flash translation layer (FTL) characteristics. Our approach improves space utilization by representing each logical block number (lbn) by one counter in the hash table, and achieves an extremely low error rate by choosing the degree of the hash function based on the address space of the flash memory. In addition, we achieved lower false identification rates. We demonstrate the performance using mathematical analysis and trace-driven simulation.

In the current research on data query for two-tiered WSN, the privacy-preservation range query is one of the hotspots. However, there are some problems in the existing researches in two-tiered wireless sensor networks such as high computational and communication costs for security comparison items and high energy consumption of sensing nodes. In this paper, a privacy-preservation range query protocol based on the integration reversal 0-1 encoding with Bloom filter is researched and designed. In the sensing data submission stage, the optimized reversal 0-1 encoding, HMAC algorithm, AES encryption algorithm and variable-length Bloom filter are used for generating the maximum–minimum comparison encoding and constructing a shorter verification index chain to reduce computational and communication costs of sensing nodes; in the private data range query stage, the base station uses the HMAC algorithm to convert the plaintext query range into the ciphertext query range and sends it to the storage node. In the storage node, the bitmap encoding information of the verification index chain is calculated with the comparison rule of the reversal 0-1 encoding and it is returned to the base station together with the verification index chain and the data ciphertext that compliance with the query rule; in the data integrity verification stage, the integrity of the query results using the verification index chain and bitmap encoding is verified at the base station. In the experimental section, the Cortex-M4 development board equipped with the Alios-Things operating system as sensing node and the Cortex-A9 development board equipped with the Linux operating system as storage node are implemented in this protocol, which is compared with the existing protocols in three aspects: the number of data collected in each cycle, the length of data and the number of data dimensions. The experimental results show that the energy consumption of this protocol is lower under the same experimental environment.

Entity resolution (ER) is to find the data objects referring to the same real-world entity. When ER is performed on relations, the crucial operator is record matching, which is to judge whether two tuples refer to the same real-world entity. Record matching is a longstanding issue. However, with massive and complex data in applications, current methods cannot satisfy the requirements. A Sequence-rule-based record matching (SeReMatching) is presented with the consideration of both which attributes should be used and their importance in record matching. We have changed the Bloom filter and therefore the checking speed is greatly increased. The best performance of the algorithm makes the complexity of entity resolution O(n). And extensive experiments were performed to evaluate our methods.

In the current digitalized era, large datasets play a vital role in features extractions, information processing, knowledge mining and management. Sometimes, existing mining approaches are not sufficient to handle large volume of datasets. Biological data processing also suffers for the same issue. In the present work, a classification process is carried out on large volume of exons and introns from a set of raw data. The proposed work is designed into two parts as pre-processing and mapping-based classification. For pre-processing, three filtering techniques have been used. However, these traditional filtering techniques face difficulties for large datasets due to the long required time during large data processing as well as the large required memory size. In this regard, a mapping-based neural skyline filtering approach is designed. Randomized algorithm performed the mapping for large volume of datasets based on objective function. The objective function determines the randomized size of the datasets according to the homogeneity. Around 200 million DNA base pairs have been used for experimental analysis. Experimental result shows that mapping centric filtering outperforms other filtering techniques during large data processing.

Blockchain provides privacy and security without centralized authority. This work analyzes cryptography applications in blockchain and analyzes efficient sharing of health records using Attribute-Based Encryption (ABE). Secure data sharing of sensitive information stored in cloud is very important as anybody can access it from anywhere in the world. This work aims to provide a secure sharing of medical records in cloud using enhanced Ciphertext Policy (CP-ABE) and Key Policy (KP-ABE) techniques along with data owner specifying the access control policies. In an open-networked system, machine learning algorithms are effectively used in pharmacovigilance to find the Adverse Drug Reaction (ADR). Securing this sensitive data in cloud is very important as PHRs privacy concern is very important.

Nodes in a sensor networks may be lost because of power exhaustion or malicious attacks. Therefore it's necessary for new nodes' deployment. Using Bloom Filter, we propose an access control protocol including the mutual authentication procedure and common key generation. It's efficient for the nodes' addition, deletion and authentication. At last, the paper makes an analysis of the performance and safety about the proposed protocol.