Database Reference
In-Depth Information
protection methods have already been shown not applicable to big data. In particular,
big data safety is confronted with the following security related challenges.
Big Data Privacy
: In the big data era, data privacy includes two aspects: (a)
the protection of personal privacy, as the advances on data acquisition is made,
personal interests, habits, and body properties, etc. of users may be more easily
acquired, of which the user may not be aware. (b) Personal privacy data may
also be leaked during storage, transmission, and usage, even if acquired with
the permission of users. Facebook is deemed as a big data company with the
most SNS data currently. Organizations that own big data usually attempt to
mine valuable information in the data with advanced algorithms. The privacy data
protection technology therefore is of great importance. According to a report [
1
],
Ron Bowes, a researcher of Skull Security, acquired data in the public pages of
Facebook users who fail to modify their privacy setting using an information
acquisition tool. Ron Bowes packaged such data into a 2.8 GB package and
created a BT seed for others to download. The analysis capacity of big data may
lead to privacy mining from seemingly simple information. Therefore, privacy
protection in the big data era will become a new and challenging problem.
Data Quality
: Data quality influences big data utilization. Low quality data
wastes transmission and storage resources, and may not be usable. There are a
lot of factors that may restrict data quality, for example, generation, acquisition,
transmission, and transmission may all influence data quality. Data quality is
mainly manifested in its accuracy, completeness, redundancy, and consistency.
Even though a lot of measures have been taken to improve data quality, the
quality related problems could not be completely solved. Therefore, effective
methods to automatically detect data quality and repair some damaged data need
to be investigated.
Big Data Safety Mechanism
: Big data brings challenges to data encryption due
to its large scale and high variety. The performance of previous encryption
methods on small and medium-scale data could not meet the demands of big
data; efficient big data cryptography approaches shall be developed. Effective
schemes for safety management, access control, and safety communications
shall be investigated for structured, semi-structured, and unstructured data. In
addition, under the multi-tenant mode, isolation, confidentiality, completeness,
availability, controllability, and traceability of tenants' data should be enabled on
the premise of efficiency assurance.
Big Data Application in Information Security
: Big data not only brings chal-
lenges to information security, but also offers new opportunities for the devel-
opment of information security mechanisms. For example, we may discover
potential safety loopholes and APT (Advanced Persistent Threat) after the
analysis of the big data in the form of log files of an Intrusion Detection
System. In addition, virus characteristics, loophole characteristics, and attack
characteristics, etc. may also be more easily identified through the analysis of
big data.
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