Security and privacy aspects in MapReduce on clouds: A survey

Abstract

MapReduce is a programming system for distributed processing large-scale data in an efficient and fault tolerant manner on a private, public, or hybrid cloud. MapReduce is extensively used daily around the world as an efficient distributed computation tool for a large class of problems, e.g., search, clustering, log analysis, different types of join operations, matrix multiplication, pattern matching, and analysis of social networks. Security and privacy of data and MapReduce computations are essential concerns when a MapReduce computation is executed in public or hybrid clouds. In order to execute a MapReduce job in public and hybrid clouds, authentication of mappers-reducers, confidentiality of data-computations, integrity of data-computations, and correctness-freshness of the outputs are required. Satisfying these requirements shield the operation from several types of attacks on data and MapReduce computations. In this paper, we investigate and discuss security and privacy challenges and requirements, considering a variety of adversarial capabilities, and characteristics in the scope of MapReduce. We also provide a review of existing security and privacy protocols for MapReduce and discuss their overhead issues.

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

“…As the most popular tool for cloud-based big data analytics, Google's cloud computing platform [193] consists of GFS [26] (big data storage), BigTable [20] (big data management) and MapReduce (cloud computing), which was discussed in the previous section. GFS is a distributed file system and it is enchanced to meet the requirements of big data storage and usage demands of Google Inc.…”

Secret sharing for cloud data security: a survey

“…With users seeking to reduce costs in the cloud's pay-as-you-go pricing model, achieving the best tradeoff between data security and access power and efficiency is a great challenge [22,81]. Existing surveys about distributed data security list security services in distributed storage: authentication and authorization, availability, confidentiality and integrity, key sharing and management, auditing and intrusion detection, and finally useability, manageability and performance [58,29]. Then, network file systems, cryptographic file systems and storage intrusion detection systems are discussed and compared.…”

“…Then, network file systems, cryptographic file systems and storage intrusion detection systems are discussed and compared. This precloud review is complemented by a thorough comparison of storage-centric data protection (i.e., network storage devices) in user-centric data protection systems (i.e., cryptographic storage systems and cloud-based storage) [93,29]. Finally, [91,29] provide a short overview of what should be done in terms of data auditing and encryption in the cloud.…”

Emerging Technologies in Big Data

“…Another lacking field of the research is holistic frameworks i.e., frameworks that solve more than a single problem, especially solving both the security and privacy aspects, and integrating some of the mentioned algorithms and frameworks, which provide computational security and privacy of data for MapReduce computations. It is believed that in the future, it will have MapReduce frameworks that provide multiple types of computations in information secure manner [12].…”