# DATA SECURITY & PRIVACY IN CLOUD COMPUTING: Concepts and Emerging Trends

Rishabh Gupta
Department of Computer Applications
National Institute of Technology
Kurukshetra, India
<EMAIL_ADDRESS>
Deepika Saxena
Department of Computer Applications
National Institute of Technology
Kurukshetra, India
<EMAIL_ADDRESS>
Ashutosh Kumar Singh
Department of Computer Applications
National Institute of Technology
Kurukshetra, India
<EMAIL_ADDRESS>
This work is supported by the University Grant Commission, New Delhi, India,
under the scheme of National Eligibility Test-Junior Research Fellowship (NET-
JRF) with reference id-3515/(NET-NOV 2017)The authors would like to thank
National Institute of Technology Kurukshetra, India for financially supporting
this research work.

###### Abstract

Millions of users across the world leverages data processing and sharing
benefits from cloud environment. Data security and privacy are inevitable
requirement of cloud environment. Massive usage and sharing of data among
users opens door to security loopholes. This paper envisages a disuscussion of
cloud environment, its utilities, challenges, and emerging research trends
confined to secure processing and sharing of data.

_Keywords_ Cloud computing $\cdot$ Security $\cdot$ User-Privacy $\cdot$ Data
Protection

##  1 Introduction

In 2006, San Jose proposed cloud computing’s concepts in Search Engine
Strategies (SES), and the National Institute of Standards and Technology
(NIST) gave a formal definition of cloud. Almost every type of organization
aspires to use cloud computing technology because of its features such as
storage, computation, flexibility, scalability, etc [1]. At present, data is
developing at a faster pace. Indeed, even excessive information is being
created every second, and likewise, the number of Internet-associated gadgets
is increasing gradually [2], [3]. As the users may not have sufficient space
to store their information, they need to keep their data on the cloud server
and access the services facilitated by the cloud [4], [5]. Cloud storage gives
an effect such that there is a vast space to store information and retrieve
the data easily. Users have to pay for each utilized cloud service [6], [7].
To make cloud computing possible and available to end-users, some services and
models function behind the scenes. Fig. 1 shows two kinds of cloud computing
models: deployment models and service models:

Cloud Charactertics Service Models Deployment Models On-demand self service
Broad network access Resource pooling Rapid elasticity or expansion Measured
service SaaS PaaS IaaS Public Private Hybrid Community $\dots$ Figure 1: Cloud
Computing (NIST)

The deployment models pertain to where the cloud infrastructure is located and
managed. There are three commonly used cloud deployment models: public,
private, and hybrid cloud. Another type of model is the community cloud which
is commonly less used. A public cloud is a collection of computing resources
that third-party organizations provide. It supports all users who want a
computer resource, including subscription-based hardware (OS, CPU, memory,
storage) or software (application server, database) to be used. A private
cloud is typically one organization’s infrastructure. The organization or a
service provider can manage such infrastructure to help different customer
groups. A hybrid cloud is a combination of private as well as public cloud
computing resources. An additional model is a community cloud that shares in
several organizations computing resources, and it is possible to manage either
through organizational IT resources or third-party vendors [8], [9], [10].
Cloud service models portray how customers can access the cloud [11]. A
combination of IaaS (infrastructure as a service), PaaS (platform as a
service), and SaaS (software as a service) are the most basic service models.
These service models can synergize; for instance, PaaS relies on IaaS because
application systems require physical facilities [12].

The model of IaaS offers customers parts of the infrastructure, including
virtual machines, storage, networks, firewalls, and load balancers components
[13]. Customers have direct access to the lowest-level software like the
virtual machine operating system, firewall, and load balancer dashboard in the
stack with IaaS. Amazon Web Services is one of the largest IaaS providers. The
PaaS model provides the customer with a pre-constructed application platform.
Customers do not need to spend time building their applications ’ underlying
infrastructure. PaaS solutions usually offer an API that contains several
features for the management and creation of programmatic platforms. Google
AppEngine is one of the examples of a PaaS provider. SaaS offers prepared
online software solutions. The SaaS software provider fully controls the
application software. Online mail, project management systems and social media
platforms are examples of SaaS applications [14].

In 2012, Cisco Bonomi coined the term “fog computing”. The idea of fog
computing is to bring the cloud closer to the devices. It is a distributed
paradigm, and an extension of cloud computing. Fog computing’s primary goal is
to address the issues that cloud computing encounters during data processing.
Fog is not a replacement for cloud computing; rather, these two technology
complements each other. Routers, switches, intelligent devices, access points,
and gateways are the essential parts of the fog layer. These devices can store
data, compute, route, and forward packets. The fog layer is the intermediate
layer between user and cloud, as demonstrated in Fig. 2. This layer has rights
of semi-permanent capacity that permit the impermanent information and data
computation capability. The use of this layer has many advantages as low
latency, higher real-time, broader geographical distributions. Moreover, it is
observed that due to neighborhood information, the routing and storage costs
in the fog environment are lesser than in the cloud environment [15]. All of
these benefits make fog computing more suitable for applications that are
sensitive to delay.

Figure 2: Fog Computing

##  2 Motivation

The paradigm of cloud computing has enabled various users to share resources
[16], [17], [18]. This notion has achieved broad popularity over the few years
by constantly increasing the number of customers and supporting infrastructure
development [19]. For instance, Organizations like Cisco expect to have more
than 24 billion gadgets connecting with the Internet by 2020. Further, Morgan
Stanley has also predicted that almost 75 billion Internet devices will be in
use by 2021. Cloud computing provides security to the customers, preventing
their sensitive information and personal data from unauthorized stakeholders
and multiple parties. Furthermore, data providers can monitor their outsourced
data privacy at any moment. Organizations do not need to worry about data
security because this facility is accessible as a service nowadays.

Cloud computing is a growing technology that provides massive information
without upfront investment to organizations with a novel business model [20]
[21], [22]. However, most organizations are still reluctant to explore their
business across the cloud because of security. When the data is stored in the
cloud server, the consequences are data ownership and management isolation
[23]. Therefore, the Cloud service provider (CSP) can search and access data
freely on the cloud server without taking the permission of the user.
Meanwhile, the cloud server might be assaulted by an aggressor to get the
user’s information. There are many security issues in the cloud environment,
such as man-in-the-middle attacks, data leaks, etc [24], [25], [26]. All the
above problems are exceptionally perils for user’s information. Losses of the
information of users is hampered, and there may occur data leakage problems
too. If the user directly uploads data on the cloud, there may be issues like
high bandwidth requirement, high latency, and a large volume of data. These
are the biggest obstacles limiting cloud development. Therefore, it is
necessary to work on it and find the right solution.

Fig. 3 exhibits consumer fraud and identity theft complaints lodged during the
period ranging from 2016 to 2020. It is observed that 4.8 million identity
theft and fraud complaints were received in the year 2020. Out of these, 1.4
million complaints were identity theft, 25 percent of cases also reported
money loss amounting, and remaining 2.2 million were fraud complaints. The
number of complaints in 2020 is increased up to 46 percent over the previous
year.

Figure 3: Identity Theft And Fraud Reports, 2016-2020
Source: Federal Trade Commission, Consumer Sentinel Network

To address the problems mentioned above, the state-of-art encryption schemes
[27], [28] were used to protect the users’ data. These schemes enable the
users to encrypt their data and store it on the cloud servers. Afterward,
cloud servers can carry out computations on it. But, Cloud servers are
presently unable to provide innovative perspectives to users while fully
maintaining their privacy. Therefore, efforts must be made to answer the
question: “How can data consumers profit from cloud computing without
compromising data privacy?”.

##  3 Privacy-Preserving Based on Cryptography Mechanism

Yuan and Yu [29] proposed a multiparty Back-Propagation Neural (BPN) network-
based approach that is accurate, efficient, and secure for collaborative
learning over arbitrarily partitioned data. To conduct operations over
ciphertexts, they used a doubly homomorphic encryption technique. But they
concentrated on enhancing data processing rather than the algorithm’s
efficiency. Zhang et al. [30] proposed a privacy-preserving deep computation
model based on homomorphic encryption. They used the offloading of the
expensive operations to enhance the learning features of the cloud. The
exponential process required by the Sigmoid function was utilized using the
Taylor theorem. However, the model only includes addition and multiplication
operations. Yonetani et al. [31] introduced a privacy-preserving mechanism
based on a double-permitted homomorphic encryption (DPHE) scheme, which
effectively learns visual classifiers across distributed private data. This
scheme provided multiparty protected scalar products while minimizing the
computational cost for high-dimensional classifiers. Nevertheless, either
addition or multiplication operation can only support at a time. A deep
learning system based on additively homomorphic encryption was presented in
[32]. The proposed system protects gradients from the curious server.
Asynchronous stochastic gradient descent (ASGD) trained overall participants’
combined datasets obtained the same accuracy as the related deep learning
system. However, the modified parameters are decrypted by the owner’s secret
key; thus, their model does not ensure parameter privacy. A basic scheme based
on multi-key fully homomorphic encryption (MK-FHE) mechanism was introduced in
[33]. The authors devised an advanced model for learning encrypted data in the
cloud that uses the double decryption mechanism and fully homomorphic
encryption (FHE) mechanism. But this scheme has a high cost in terms of
computation and communication. To perform the deep neural network algorithms
over encrypted data, a framework named CryptoDL was proposed by Hesamifard et
al. [34]. To address the existing limitations of homomorphic encryption
schemes, they designed neural networking techniques. However, the proposed
algorithm protects the owner’s data by using keys that is not practical. A
privacy-preserving outsourced classification in cloud computing (POCC)
framework was presented in [35], which effectively enable an arbitrary number
of multiplication and addition operations on ciphertexts. The data and query
were protected by providing a proxy fully homomorphic encryption based on
Gentry’s scheme. Nevertheless, the cost of calculation and communication was
increased in the proposed framework. Ma et al. [36] proposed a privacy-
preserving deep learning model, namely PDLM, to train the model over the data
encrypted by the owners’ keys. A privacy-preserving calculation toolset based
on stochastic gradient descent (SGD) was utilized to accomplish the training
task in a privacy-preserving way. Although the model minimized storage
overhead, it has a high computation cost and lower classification accuracy. A
privacy-preserving outsourced classification scheme is presented in [37],
which provides the classification services over encrypted data for users. They
also designed two concrete secure classification protocols for the Naive Bayes
classifier and the hyperplane decision-based classification, respectively. But
during the launch of a classification query, user interactions are often
involved in this scheme. Gao et al. [38] proposed a privacy-preserving Naive
Bayes classifier scheme that prevents information leakage under the
substitution-then comparison (STC) attack. A double-blinding technique was
adopted to protect the Naive Baye’s privacy. Both the communication and
processing overhead were decreased, but unable to discover the truth while
maintaining privacy. Phong and Phuong [39] proposed two systems, namely the
Server-aided Network Topology (SNT) system and the Fully-connected Network
Topology (FNT) system based on the connection with SNT and FNT server to
protect the SGD privacy. The SNT and FNT systems achieved an accuracy
corresponding to SGD using weight parameters instead of gradient parameters.
These systems are both effective and efficient in terms of computing and
communication. Table 1 shows the summary of privacy-preserving of data based
on the cryptography mechanism.

Table 1: Summary of Privacy-Preserving based on Cryptography Literature Reference | Approach | Pros | Cons
---|---|---|---
Wang et al. [15] | Hash-Solomon code | High degree | High
| algorithm | privacy protection | Computation cost
Yuan et al. [29] | BGV fully and Doubly | Encrypt data efficiently | Low efficiency
| homomorphic encryption | secure scalar product |
Yonetani et al. [31] | Doubly homomorphic | Supported multi-party | Both addition and
| encryption | secure scalar product | multiplication
Li et al. [33] | Multi-key fully | Preserve privacy of | Low efficiency
| homomorphic encryption | sensitive data |
Ma et al. [36] | Distributed Two Trapdoors | More efficiency | Less accuracy
| Public-Key Cryptosystem |  |
Shokri and Shmatikov [40] | Optimization algorithms | Protect training data | High complexity
Wang et al. [41] | Homomorphic encryption | Encrypted data by randomly | Either addition or
|  | splitting numerical | multiplication
Chan et al. [42] | Homomorphic and | Partitioned Data | Two party
| ElGamal scheme | vertically |
Bansal et al. [43] | Homomorphic and | Partitioned Data | Two party
| ElGamal scheme | arbitrarily |
Samet et al. [44] | BPNN and Extreme | Partitioned Data horizontally, | High Communication
| learning machine scheme | vertically with multi parties | cost
Cao et al. [45] | KNN,TF-IDF and | Solve the problem of muti- | Does not explore
| Dynamic Data Operation | Keyword ranked search | checking integrity
Guo et al. [46] | TF-IDF model | Update the outsourced data | Does not improve
|  | at less cost | computational efficiency
Fu et al. [47] | Stemming algorithm | Outsourced Data With | Does not reflect
| Accuracy Improvement | at less cost | the keyword weight
Fu et al. [48] | Keyword based | Use two cloud server | High
| Search Scheme | for store and compute | Compleaxity
Huang et al. [49] | Public key authenticated | Handle to resist | Single
| encryption | inside keyword | keyword
Qi et al. [50] | Locality-Sensitive | Handle the service | Low
| Hashing | recommendation | efficiency

##  4 Privacy-Preserving Based on Perturbation Mechanism

Dwork et al. [51] first proposed differential privacy and obtained complete
background knowledge under the attacker’s hypothesis. To protect data privacy,
the randomly generated noise is disturbed according to a specially selected
distribution. Fletcher and Islam [52] proposed a differential privacy
decision-making random forest algorithm to reduce the query times and
sensitivity. This scheme also minimizes the amount of noise that must be
appended to protect the privacy and improve data availability. However, there
is no consideration for the distributed scenario where multiple data owners
conduct collaborative data mining. To perform privacy-preserving machine
learning over cloud data from different data providers, Li et al. [53]
proposed a scheme that protects the data sets of various providers and the
cloud. They used the public-key encryption with a double decryption algorithm
(DD-PKE) to encrypt the data sets of the different providers with different
public keys and $\epsilon$-differential privacy to add statistical noises into
data to protect the privacy. Their scheme improved computational efficiency
and data analysis accuracy. But such fully homomorphic (FHE) encryption
schemes are commonly low efficiency. A privacy-preserving Naive Bayes learning
scheme with various data sources is presented in [54]. The proposed scheme
enabled a trainer to train a Naive Bayes classifier over the dataset provided
jointly by different data owners without the help of a trusted curator.
However, collaboration is permitted, or adversaries can forge and modify the
data in this scheme. A distributed agent-based privacy-preserving framework,
namely DADP, was proposed by Wang et al. [55]. The proposed framework collects
real-time spatial statistics data and publishes it with an untrusted server.
To achieve global $w$-event $\epsilon$-differential privacy in a distributed
manner, they utilized a distributed budget allocation mechanism and an agent-
based dynamic grouping mechanism. The noise is added to crowd-sourced data
using the Laplace technique in DAPM. It started a batch of reliable proxies
(Agents) and anonymous connection technology to safeguard users’ privacy under
an untrusted server. Therefore, it was regarded as a semi-centralized setting
and resulted in a more complex system. An efficient privacy-preserving scheme
based on machine learning was proposed by Hassan et al. [56]. Authors adopted
a partially homomorphic encryption technique to encrypt data, and noised is
added by applying a differential privacy mechanism. It allows all parties to
publicly check the ciphertext’s correctness via a low-cost unidirectional
proxy re-encryption (UPRE) mechanism. However, the proposed system shared
fewer data. A private decision tree algorithm based on the noisy maximal vote
was introduced in [57]. To strike a balance between accurate counts and noise,
an effective privacy budget allocation approach was utilized. The main aim of
constructing an ensemble model is to increase the accuracy and stability by
using differential privacy. The proposed algorithm performs the privacy
analysis on each individual tree rather than the ensemble as a whole. Gupta et
al. [58] proposed a machine learning and probabilistic analysis-based model,
namely MLPAM. The authors used encryption, machine learning, and probabilistic
approaches to share the multiple participants’ data and minimize the risk
affiliated with the leakage for prevention and detection. However, MLPAM is
not able to give protection to the classifier. To preserve the privacy of the
data as well as query processing, Sharma et al. [59] proposed a Differential
Privacy Fuzzy Convolution Neural Network framework, namely DP-FCNN. The
Laplace mechanism was utilized to inject the noise and encrypt the data by
applying the lightweight Piccolo algorithm. The key properties were extracted
using the BLAKE2s technique. But, DP-FCNN enhanced the computational overhead.
Table 2 summarises the privacy-preserving data using the Perturbation
Mechanism.

Table 2: Summary of Privacy-Preserving based on Perturbation Literature Reference | Approach | Pros | Cons
---|---|---|---
Fletcher and Islam [52] |  $\epsilon$-Differential Privacy | Reduce query time | Less accuracy
Li et al. [53] | Homomorphic encryption | More efficiency | High Computation
| Differential Privacy |  | cost
Li et al. [54] |  $\epsilon$-Differential Privacy | Preserve privacy of data | Forge data
Wang et al. [55] |  $w$ event | Data protection | Complex system
|  $\epsilon$-Differential Privacy |  |
Hassan et al. [56] | Homomorphic encryption | Low cost | Limited data sharing
|  $\epsilon$-Differential Privacy |  |
Liu et al. [57] | Laplace mechanism | Balance between | Individual privacy
|  | accuracy and noise |
Gupta et al. [58] | Gaussian mechanism | High accuracy | No classifier
|  |  | protection
Sharma et al. [59] | Laplace mechanism | Data protection | Computation overhead
|  |  | protection

##  5 Research Gaps

On the basis of the literature review, the following research gaps are
identified.

  1. 1.

Less protection, and privacy of the outsourced data.

  2. 2.

The efficiency of methods to protect privacy must be increased.

  3. 3.

Multiple user-based protection techniques are required.

  4. 4.

Computational and communication costs during the data transfer must be
reduced.

  5. 5.

Minimization of the threats of data leakage during transmission.

##  6 Research Objectives

To fill the identified study gaps, the goal described below is developed.

  1. 1.

To optimize the computation and communication costs among different entities.

  2. 2.

To reduce data leakage by using advanced encryption techniques.

  3. 3.

To solve the security issue of cloud computing.

  4. 4.

To formulate the approach to reduce the latency while preserving data privacy
during transmission.

##  7 Justification for the objectives

In an age of quickly growing information generation, it requires a computing
infrastructure that can store and process enormous amounts of information.
Developing these new cloud-based methods defines current safety and privacy
demands and addresses consumer issues facing quality, effectiveness, and the
best use of consumer needs. We provide complete awareness of secure cloud and
efficient use of resources to validate our methodologies. Because security
helps prevent data leakage and the disposal of data. With the following
benefit, the advances to be carried out in research work will open an era for
enhanced cloud facilities:

  1. 1.

More quality of service (QoS)

  2. 2.

Better resource utilization

  3. 3.

Improves confidence in cryptography services

  4. 4.

Low the cost of computation of the process

  5. 5.

Less service level agreement (SLA) violation

  6. 6.

Better use of the cloud for securely sharing of data among the organizations.

  7. 7.

Better efficiency

  8. 8.

Saving the cost

  9. 9.

Access the file universally

  10. 10.

Increase the security

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