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Big Data Analytics: Security and Privacy Challenges Youssef Gahi, Mouhcine Guennoun, Hussein T. Mouftah

School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave., Ottawa, ON, Canada

[email protected], [email protected], [email protected]

Abstract—The digitalization of our day-to-day activities has resulted in a huge volume of data. This data, called Big Data, is used by many organizations to extract valuable information either to take marketing decisions, track specific behaviors or detect threat attacks. The processing of such data is made possible by using multiple techniques, called Big Data Analytics, which allow getting enormous benefits by dealing with any massive volume of unstructured, structured and semi-structured content that is fast changing and impossible to process using conventional database techniques. However, while Big Data represents an immense opportunity for many industries and decisions makers, it also represents a big risk for many users. This risk arises from the fact that these analytics tools consist of storing, managing and efficiently analyzing varied data gathered from all possible and available sources. The consequence is that people become widely vulnerable to exposure because of combining and exploring specific behavioral data. That is, it is possible to collect more data than it should have which leads to many security and privacy violations. Therefore, research community has to consider these issues by proposing strong protection techniques that enable getting benefits from big data without risking privacy. In this paper, we highlight the benefits of Big Data Analytics and then we review challenges of security and privacy in big data environments. Furthermore, we present some available protection techniques and propose some possible tracks that enable security and privacy in a malicious big data context.

Keywords- Big Data Analytics; Security; Privacy; Homomorphic Encryption ; Blind Processing.

I. INTRODUCTION The digitalization of day-to-day life by adopting the use of

smart phones, computers, connected watch, Internet, Social networks, connected objects and streaming technologies, has resulted in a huge volume of digital content that is exponentially increasing every day. This content, which is called Big Data, serves as a good basis for many organizations in different sectors that intend to automatically extract strategic information in good time.

Big Data is characterized by any storage that has large volumes, high velocity, valid, varied and valuable content such as navigation history, cookies, social networks, camera records, watched videos and gathered personal information. This kind of storage allows industries to extract valuable knowledge at the right time and even in real time. It is becoming a crucial way for leading companies to achieve their objectives by adapting the portfolio to fit customer needs, and allowing governments predicting unexpected threat attack. However, processing and analyzing such huge amount of heterogeneous data is not possible by using structured databases and

conventional methods, since it requires massive parallel processing techniques, thus, it was mandatory to introduce novel techniques and tools, that are well aligned with this evolution. These tools and techniques are called Big Data Analytics.

Big Data Analytics is the use of advanced analytic and parallel techniques to process very large and diverse records that include different types of contents. Big Data Analytics tools allow getting enormous benefits and valuable insights by dealing with any massive volume of mixed unstructured, semi- structured and structured data that is fast changing and difficult to process using conventional database techniques. However, while Big Data represents an immense opportunity for many industries and decisions makers, it also represents a big challenge for respecting privacy and security concerns. This challenge arises from the fact that these analytics tools consist of storing, managing, analyzing, visualizing, and sharing varied data gathered from all possible and available sources. The consequence is that Internet users become widely vulnerable to exposure because of combining and exploring specific behavioral data. That is, it is possible to collect more data than these tools should have and extract much more than what one could imagine which leads to many security and privacy violations.

Privacy and security of these collected records are of very importance and high priority. Therefore, the research community has to consider these concerns by proposing and implementing strong protection mechanisms that enable getting benefits from big data without risking security and privacy. Indeed, some first attempts that aim at providing a secure layer when dealing with big data have been already proposed, but other possible solutions should be explored to cover all possible tracks in order to build a strong big data platform.

In this paper, we give an overview of Big Data and Big Data Analytics, we highlight then some potential security and privacy challenges that are related to this technology. Furthermore, we propose some possible tracks that could provide a balance between extracting useful information and respecting security and privacy in big data analytics.

The rest of this paper is organized as follows. In Section II, we give an overview of Big Data and Big Data Analytics concepts. In Sections III, we highlight some potential security and privacy issues related to big data. In Section IV, we present some related work attempting to secure the big data concept. In Section V, we propose some possible solutions that enable a privacy protection in a big data context. Finally, Section VI

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978-1-5090-0679-3/16/$31.00 ©2016 IEEE

concludes the paper by providing some future perspectives towards a secure big data environment.

II. BIG DATA AND BIG DATA ANALYTICS

A. Big Data Big data is a new concept that describes any volume of

heterogeneous data that is not possible to process by using the conventional database techniques. It consists of different kinds of digital content:

1. Structured data:

Structured data is all type of data that is easy to model, enter, store, query, process and visualize. It is generally presented as pre-defined fields, with specific types and sizes, managed in relational databases or spreadsheets. Data with rigid structure makes easy extracting useful information since the processing does not require high-performance capabilities or parallel techniques.

2. Semi-structured data:

Semi-structured or self-describing data is a type of structured data, but it does not follow only a rigid model. In addition to the model defining the structure, it contains a kind of meta- model such as tags and markers that are used to identify certain elements and define a hierarchical representation of different fields within the data. Well-known examples of semi-structured data are XML (Extensible Markup Language) and JSON (JavaScript Object Notation).

3. Unstructured data:

Unstructured data is a type of records that is presented and stored without predefined format. It is typically composed of free form text such as books, articles, documents, emails, and media files such as image files, audios, and videos. The fact that is difficult to represent such kind of data in a rigid form makes it difficult to process which leads to introduce new processing mechanisms such as NOSQL.

It is worth noting that the definition of big data is continuing changing to include new details, which are becoming very important to consider. A good big data is measured by the following criteria:

1. Volume

It is the data collected and stored in many distributed data stores. It is usually a huge amount of content scaled to Exabytes that is available for processing in order to extract valuable knowledge. The more this volume is bigger, the more it is significant for processing but with respecting the four coming rules: Variety, Veracity, Value, and Velocity.

2. Variety

One of the most important criteria in big data is the variety of the content. As the data could be structured or not, it also could be internal or external. The internal data is gathered from the internal resources in the organization such as CRM, internal databases, ERP, etc. Whereas the external data is gathered from open sources such the web, open big data, etc. This variety allows processors to extract as interesting as varied information about a specific topic. However, new processing

techniques and specific architectures have to be implemented in order to deal with these heterogonous records whereby the Veracity should be carefully considered.

3. Veracity

The accuracy and validity of the collected data have the utmost importance. It is worth mentioning that a large volume of data that are not accurate or valid cannot serve as a basis for extracting insights. Rather, it can lead to a false interpretation. For this, very large volumes of data and the heterogeneity of sources need strictness in both the organization of the collected records and the crosscheck to remove any doubt about gathered data while guarantying the integrity and the value of the processed inputs.

4. Value

It is true that the volume, variety, and veracity are important features for big data. However, what is most important is to be able to use it to extract value and in a reasonable time (Velocity).

5. Velocity

The velocity refers to the speed at which the data is generated and how it is changing. Big data does not only rely on static records but it also uses real-time streams and without storage. That is, processing big data has to be able to generate and extract the result or the visualization in few seconds or few milliseconds in the case of critical applications.

Although the evolution of big data can lead to a revolution in organizations and enterprises, the aforementioned five criteria brought the needs to find tools and mechanisms for efficiently processing and analyzing the data. This kind of techniques is called Big Data Analytics.

B. Big data Analytics The technological world of big data is based on specific

frameworks that make big data volumes beneficial. These frameworks consist of a set of systems deployed over multiple parallel nodes and clusters and allow performing huge computations on reduced infrastructure. Apache Hadoop is the first and the most known tool for processing big data [21]. Hadoop ecosystem is formed by many open source components developed by Apache. In Table 1, we describe some of these components as well as other analytics techniques.

TABLE I. ANALYTICS TECHNIQUES

Analytics technique

Description

HADOOP

Hadoop analytic tool is an open source software mainly conceived for big data. It is a non-relational database architecture dealing with a very big amount of distributed heterogeneous data. It serves as a good basis for other software that aims at performing parallel computations on a large content. Hadoop offers two main capabilities, storage, and computations. Hadoop stores the data into

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distributed nodes in different clusters, it processes then this data using the MapReduce technology.

MAP- REDUCE

MapReduce is a Google technology designed to process large data. Its working mode is divided into two parts: Map and Reduce. While The first part consists of distributing the input data to several clusters for parallel processing. The Reduce part consists of collecting all sub-results to provide one final feedback.

HDFS

Hadoop Distributed File System is the core of Hadoop. It is a distributed file-system for storage and management of data warehouses. HDFS stores large files scaled to petabytes across multiple distributed nodes or clusters. To do this, it splits the input data into blocks and then it allocates these blocks on servers in different locations.

Hive

Hive is a data warehouse tool that allows requesting and managing vast distributed data. Hive provides capabilities to access the storage using SQL-like language called HiveQL.

HCatalog

HCatalog is a key component of Hive. It is a table management system enabling the storage of data in any format regardless if it is structured or not.

HBase

HBase is the Hadoop DataBase. It is a distributed database management system, which is inspired from Google's BigTable. HBase is very powerful for management and processing of big tables with more than millions of columns.

PIG

PIG is a platform that allows developing MapReduce programs. The programming language is called ‘PIG Latin’. It aims to increase the performance of Hadoop and MapReduce by offering a programming language allowing faster processing.

Mahout

Mahout is a software developed over Hadoop using MapReduce paradigm, it includes a set of useful algorithms for filtering, classification, and clustering a distributed dataset.

CASSAN- DRA

Cassandra is a tool developed by Facebook; it is a column-oriented NoSQL database. Casandra supports MapReduce processing and is particularly known for its ability to facilitate data access for a large volume of records.

IN- MEMOR-

Y

In-memory processing refers to treatments that are performed in the RAM of the equipment, rather than on hard disks. The advantage of in- memory processing is that it allows immediate access to the right information. However, these data are not stored in the long term,

which can pose archiving problems.

NOSQL

Not Only SQL is all databases that are not relational. It provides capabilities to query and retrieves unstructured and semi-structured data.

III. SECURITY AND PRIVACY CHALLENGE The big data is a recent technology, which was rapidly

adopted by many industries to predict market trends and users’ behavior. As the basic role of big data tools is the storage and processing of huge volumes, these tools have not yet reserved enough space to cover some relevant topics such as security and privacy protection. Despite this, the organizations focus too much on the advantages offered by Big Data Analytics such as Hadoop, and too little on the concerns related to security and privacy protection.

There is a set of privacy and security concerns that must be considered before building a big data environment. In what follows, we highlight the most important challenges that should be taken into consideration when dealing with big data.

1. Random Distribution

The concept of big data analytics is mainly based on parallelism, for this, the large data is stored and processed in different clusters, which are a set of distributed servers around the world and acting as one powerful station. The main issue with this topology is it is very hard to know the exact location of storage and processing which can result in many security problems and regulation breaches. The main challenge with big data solutions is to be able to distribute storage and processing according to the regulations and data sensibility.

2. Privacy

Another challenge for big data technologies is to assign to sensitive data the special attention that it should have. Current big data analytics treat all data with the same priority and do not associate special actions, like encryptions or blind processing [16], to that kind of data. Thus, if a hacker or a malicious node gain access to the clusters it would be easy to steal, bad exploit or alter the contained records.

3. Computations

The main idea behind big data is to extract useful insights by performing specific computations. However, it is important to secure and protect these computations to avoid any risk or attempt to change or skew the extracted results. It is also important to protect the systems from any attempt to spy on the nature or the number of performed computations.

4. Integrity

In an open context like big data, a large volume of content is not always a good metric for the quality of extracted results. That is, before searching for insights and making decisions based on big data, it is important to ensure the validity and the trust level of that data in order to avoid relying on a suspect or compromised records.

5. Communication

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Big data is stored in several nodes belonging to many clusters which are distributed around the world. All communications between clusters and nodes are assured through ordinary public and private networks. However, if someone can modify the inter-node communication it would be easy to extract valuable information. Therefore, it is a good challenge for big data tools to adopt new secure network protocols in order to protect interactions between different parties.

6. Access Control

In a big data context, access to the data should be managed by a strong access control system to deny any malicious party from getting access to the storage servers. That is, the only node with sufficient administrative rights could have the possibility to manage and process any content. Furthermore, any modification in clusters’ state such as addition or deletion of nodes should be monitored by an authentication mechanism to protect the system from malicious nodes.

IV. SECURING BIG DATA ANALYTICS The big data analytics were rapidly adopted by many

organizations even before ensuring that all aspects are considered, especially the ones related to security and privacy. For this reason, the scientific community has attributed a great attention to this growing area while trying to redesign and improve the basic concept. However, most of the proposed improvements intend to enhance the processing and storage techniques as well the adopted architectures [1-8], but few works focused on enabling security and privacy aspects. In what follows, we review some of the interesting contributions looking at enhancing privacy and security in big data environments.

M. Li et al have presented in [9] an architecture that enables protecting privacy issues in cloud environments and big data. The proposed platform consists of reducing the node authority by de-privileging them in order to deny the cloud provider from inspecting and tracing users’ activity. Furthermore, the new architecture allows the cloud users to configure their privacy protection and reduces the ability of the provider of modifying privacy settings.

In [10-12], the authors have presented the importance of protecting the large-scale storage and have highlighted a set of possible challenges that could be faced by big data. Whilst, Harsh et al. and Matthew et al. have presented big data challenges specifically for healthcare and social media respectively in [13] and [14].

In [15], the authors proposed a certificate-less proxy re- encryption scheme called CL-PRE. This latter is based on randomized and re-encryption keys and it allows a data owner to share his data while managing the access control and reducing trust for the cloud infrastructure.

By focusing rather on protecting data processing, the authors in [16-18] proposed novel techniques that aim at securing database activities and could be applied to big data analytics. In [16], the authors have introduced what we call blind processing over outsourced data. The contribution relies on homomorphic encryption [22] and consists of blindly leading a data processor to compute some actions over

encrypted data without disclosing neither the content nor the kind of data accessed. In [17], the authors rely on local clustering and propose a cryptographic-based transformation scheme that protects privacy and improve query processing in outsourced databases. In [18], Jang et al. have proposed an approach that guarantees data confidentiality and query result integrity while relying on a privacy-aware query authentication method.

In [19], the authors have designed a pay-by-data model through which the access to the usage of user-generated data is protected by using authentication services.

In [20] and based on third party auditors, the authors have proposed an unbiased trust model between service providers and cloud users. This model relies on a rank model and feedback collected from different users.

V. POSSIBLE TECHNIQUES TO PROTECT PRIVACY IN BIG DATA

The big data platform is the composition of several technological evolutions either for storage or processing capabilities. For this, traditional security techniques already employed for traditional systems cannot be efficient and directly applied to big data contexts. Therefore, new security techniques should be deployed to accompany these evolutions.

The big challenge when adding security and privacy to big data environments is to come up with balanced solutions between regulations, security controls, and analytics. In what follows, we propose a set of techniques that help and could be used as a basis for securing big data:

1. Rules and Legality Big data is a huge phenomenon that continues changing the world since it becomes an important and potential source for decision makers. Organizations and governments are benefitting from the collection, analysis, and processing of vast volumes of data from all possible sources to extract valuable insights. However, there are no enough laws and regulations that regulate the mining of big data. As this latter collect and store every digital record, it could contain financial, health and sensitive personal information. That is, no all stored data could be processed and mined for information. Furthermore, as big data works under a distributed storage mode everywhere around the world, it is important to carefully choose the storage and processing location to comply with countries’ agreements, and sometimes the differences. Therefore, a set of laws and regulations should be developed to make from big data a benefit and safe technology.

2. Encryption The encryption is always a good technique to protect sensitive data. In the case of big data, encryption could be perfectly employed to secure different components such as storage, computations and communications.

• Storage:

Big data is generally stored in the clusters as it is and without any pre-processing. Further, sensitive and not sensitive data are treated the same. So, if a malicious party gains access to the

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storage, the critical data is then easily explored. Therefore, all data or at least the most important one should be stored in an encrypted form. Thus, if malicious or unauthorized nodes get access to the storage, it cannot extract any knowledge from it since only parties possessing decryption keys can look inside.

• Computations:

Protecting the computations has as much importance as protecting the storage. As it is very difficult to predict or to even know where big data processing is performed, it is mandatory to adopt some control techniques either to deny the node from accessing the processing results or to check the node’s credibility. For this, blind processing techniques based on homomorphic encryption are a suitable solution for this need. This kind of solutions allows a processor to execute a suite of instructions without being aware of their natures.

• Communications:

The communication between all parties in big data environments should be protected to secure different exchanges. One efficient solution is to encrypt networks by employing SSL, TLS or IPSec protocols which render communications unreadable without the knowledge of the keys.

3. Authentication: Authentication mechanisms are an efficient way for controlling access to important resources. Big data solutions architecture should employ such a technique to control both, joining clusters and accessing critical storages.

4. MetaData and Tagged Data Another possible solution to differentiate the collected data according to its importance and the possibility of including in the treatment or not is the involvement of metadata and tag techniques. This way, big data tools will not treat all records the same and it would be possible to respect private information when processing.

5. Unstructured distribution If a malicious party gain access to one or some clusters, it would be possible to steal meaningful information, thus, it would be interesting to find a way to make it difficult the use of data for parties who do not have access to the global system. The distributed mode adopted by big data could be perfectly adjusted to achieve this objective. To do this, we need to avoid storing the interrelated data in the same cluster and distribute it into several ones. Also, unstructured distribution makes it is possible to separate data from individual related information and deny hackers from extracting useful insights even if they can access some nodes.

6. Anonymization Anonymization is just another possible way to protect collected big data. This main idea consists of using data perturbation and data swapping techniques to protect the association of individuals to critical information. There is also the k- anonymity which provides a measure of privacy protection by preventing re-identification of data by hiding the real position among k-1 other ones [23].

7. Tracing activity In order to protect or at least supervise the stored data, it is mandatory to log every activity performed over the big data as well as the responsible of these actions. These logs could be audited to detect if there were any malicious actions trying to manipulate the big data.

VI. CONCLUSION Big Data serves as a good basis for many organizations and

governments in different sectors that intend to automatically process and extract valuable insights in order to help decision makes. However, the fact to collect and compute all possible and varied data could lead to many security and privacy violations. In this paper, we have highlighted a set of security and privacy challenges that should be considered by big data tools. Furthermore, we presented some possible solutions and techniques that could help securing this distributed environment. As a future work, we intend to implement some of these protection techniques in an open source big data analytic tool.

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