Privacy preserving classification on local differential privacy in data centers

Biomedical research often involves studying patient data that contain personal information. Inappropriate use of these data might lead to leakage of sensitive information, which can put patient privacy at risk. The problem of preserving patient privacy has received increasing attentions in the era of big data. Many privacy methods have been developed to protect against various attack models. This paper reviews relevant topics in the context of biomedical research. We discuss privacy preserving technologies related to (1) record linkage, (2) synthetic data generation, and (3) genomic data privacy. We also discuss the ethical implications of big data privacy in biomedicine and present challenges in future research directions for improving data privacy in biomedical research.

Big Data Privacy in Biomedical Research

Investigation shows that a huge number of spatial data exists in current business databases. Traditional data warehousing and OLAP, however, could not exploit the spatial information to get deep insight into the business data in decision making. In this paper, we propose a novel approach to enabling spatial OLAP by aggregating on the spatial hierarchy. A spatial index mechanism is employed to derive the spatial hierarchy for pre-aggregation and materialization, which in turn are leveraged by the OLAP system to efficiently answer spatial OLAP queries. Our prototype system shows that the proposed approach could be integrated easily into the existing data warehouse and OLAP systems to support spatial analysis. Preliminary experiment results are also presented.

An approach to enabling spatial OLAP by aggregating on spatial hierarchy

Big Data Analytics has been gaining much focus of attention lately as researchers from industry and academia are trying to effectively extract and employ all possible knowledge from the overwhelming amount of data generated and received. Traditional data analytic methods stumble in dealing with the wide variety of data that comes in huge volumes in a short period of time, demanding a paradigm shift in storage, processing and analysis of Big Data. Owing to its significance, several agencies including U.S. government have released huge funds for research in Big Data and allied fields in recent years. This paper presents a brief overview of research progress in various areas associated to Big Data Processing and Analytics and conclude with a discussion on research directions in the same area.

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Research in Big Data and Analytics: An Overview

Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy, and data utility over large-scale data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large-scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets.

/pdf/frequent-itemsets-mining-with-differential-privacy-over-3n16h21j2y.pdf

Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data

Discovering that Map-Reduce framework is a popular way to deal with a large scale of data, but there is a significant risk to leak out users' personal information, especially when the data is sensitive, for example, including users' health records, salary information, etc. Differential privacy has recently emerged as a new paradigm for preserving private data. This makes it possible to provide strong theoretical guarantees on the privacy and utility of the query results. In this paper, we focus on top-k query which is one of the most useful queries in Map-Reduce framework over big data sets.Motivated by this, we propose an efficient algorithm, called DiffMRDifferentially private Top-kquery over MapReduce), for processing top-k query as well as satisfying differential privacy. In our algorithm, to avoid the private leak in middle process, we use exponential mechanism to select top-k records from big data sets by using score function. When the data set is too large to get a reasonably accurate result, we can reduce the reject rate and execute several more times Map-Reduce to get a more accurate top-k query result. After getting a final top-k candidate result, we will add Laplace noise to each record and adopt post-processing technique to improve the accuracy of query answers. Our experimental study demonstrates that DiffMR algorithm can be used to answer the top-k query accurately in Map-Reduce framework.

Differentially private top-k query over MapReduce

Differential privacy has emerged as one of the most promising privacy models for releasing the results of statistical queries on sensitive data, with strong privacy guarantees. Existing works on differential privacy mostly focus on simple aggregations such as counts. This paper investigates the spatial OLAP queries, which combines GIS and OLAP queries at the same time. We employ a differentially private R-tree(DiffR-Tree) to help spatial OLAP queries. In our method, several steps need to be carefully designed to equip the spatial data warehouse structure with differential privacy requirements. Our experiments results demonstrate the efficiency of our spatial OLAP query index structure and the accuracy of answering queries.

DiffR-Tree: a differentially private spatial index for OLAP query

With the development of internet and artificial intelligence, the data volume of all industries has increased exponentially. How to store these massive data has become the core problem at this stage. Taking traffic big data as an example, this paper introduces the framework of distributed storage and consistent hashing technology. At the same time, an improved consistent hashing algorithm based on virtual node is proposed. It solves the hot spot problem in network storage and the problem of data skew caused by uneven node distribution. Experimental results show that this adaptive consistent hashing algorithm based on virtual node can provide better node allocation efficiency and better adaptability.

Application of Consistent Hash Based on Virtual Node in Traffic Big Data

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Papers

Differentially private top-k query over MapReduce

DiffR-Tree: a differentially private spatial index for OLAP query

Application of Consistent Hash Based on Virtual Node in Traffic Big Data