Fully analytics. · Although, along with its benefits

Fully Homomorphic Encryption Scheme In Big Data


Prasanna Kumar Annapureddy,                                               
Ankit Dadoch           

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Texas A&M University,                                                          
Texas A&M University,

Corpus Christi                                                                          
Corpus Christi

[email protected]                                        [email protected]





Data is a term given to data that is so large and complex that conventional
data processing application software’s are not enough to deal with them.

today’s world, privacy leakage related to big data is a major issue for
individuals. Usually, data protection and data security is provided through
encrypted data but at the expense of usability.

Homomorphic Encryption allows to perform unlimited chaining of mathematical
operations on the encrypted data making it possible for legal companies and
other institutions to use them.

this paper, we address the problem of building secure computational services
for encrypted information in cloud computing without decrypting the encrypted




is an increasing need in for development of Big Data infrastructure that will
support storage and process big data in cloud computing environment.

computing amongst many of its features such as reduction in cost, elasticity,
wide network access and measured service etc. offers the most crucial feature
of storage and analysing of sharing of computing resources. This is the major
reason why it is the chosen as a tool for Big Data processing and analytics.

along with its benefits it has some risks too


A.   Cloud


computing is an Information Technology(IT) model. The IT components consist of
crucial components: hardware, software, networking and services.

computing is lacking in Structure, confidentiality, integrity and availability.

cloud computing requires protection of the cryptographic abstracted TRIAD –
confidentiality, integrity and availability. 
These three are the basis of information security.  The meaning of securing the clouds means that
the information that is stored should be free from attacks by an advocacy.

Computing has several models namely public clouds, private clouds and hybrid
clouds and all three models require some real-time security. Transmission over
public data also requires some sort of security as the client will demand some
sort of security from the service providers.

computations performed on the encrypt stored data poses certain challenges as
the private key will have to be provided to the cloud providers before computation
analysis could be carried out on that data which can be another problem as the
cloud providers will have access to your data and they could also be an

keeping in mind these challenges to the data confidentiality and user’s privacy
along with the need of application of data mining and other data analytic
algorithms to be efficiently encrypted, fully homomorphic encryption (FHE) is


B.    Big
Data Analytics Security


we already know, big data is defined as a collection of datasets that are so
large and complex that the processing of such data becomes very intricate using
conventional Data processing applications. 
Big data analytics faces several challenges such as storage, sharing,
analysis and visualisation.

 Big data is characterised with V’s
dimensions.  These include volume,
velocity, variety and voracity.  The
details of these dimensions are given below:


This term defines that Data Volume typically ranges from several           TB to ZB monthly.   This fits the enterprise security model as
it is pretty common for large organisations to collect tens of terabytes of
security data on monthly basis.

This deals with real-time data analyst requirements. In cyber security,
velocity can imply to the requirement for immediate anomaly, or rapid incident
response in real-time. Real-time data analysis is crucial here to minimise
damages that are related with cyber security attack.

This term explains that multiple data type requirements and feeds that include
structured and unstructured data combined give us the Big Data.  From a security point of view, Data variety
could include large  files,  network flows, intelligence and social
networking activity etc. So, usually enterprises will collect hundreds of
different types of data for analysing security using different algorithmic

This term implies that big data should be loyal and accurate. From the security
perspective, this means to trust confidentiality and availability of data
sources like log files and external feeds.


·      The
definitions that are given above clearly define that big data needs security
with  the help of cryptography.


  Our Proposal


·      Our
approach is concerned with developing a new model that combines the big data
analytics, cloud computing and fully homomorphic encryption to improve
security. Thus, our contribution is to merge these already existing research
entities as studied separately into some meaningful research exposition.




Key Encryption


·      In
today’s world, almost every individual has a portable computing device that can
access server in the cloud environments. So, the security of their data is of
utmost importance.  In earlier computing
scenarios, clients were trusted but they were weak, while computationally
strong servers were not trusted due to that client lack of control they had
over their operational functionalities. The improvement over this was to simply
encrypt the pieces of data which solved almost every privacy issue perfectly.

·       Data could be encrypted using symmetric key
encryption or asymmetric key encryption called public key encryption.  Nonetheless, a model was derived based on the
exponentiation function called the homomorphic encryption.  Further, we will discuss the public key
encryption used along with homomorphic encryption which are divided into two
categories namely partially homomorphic public key encryption and fully
homomorphic public key encryption.


   Partially Homomorphic Public Key Encryption


·      Encryption
allows complex mathematical operation encrypted data without the contents of
the encrypted data being revealed.

encryption scheme is defined as

E = (Gen, Enc., Dec., Eval.) with an
extension of two group operators (+, *) over the plaintext M and the ciphertext
C, respectively.

By the
definition, this is a “homomorphism” between the plaintext M and the ciphertext

If M1+M2 =
Dec (sk, C1, C2)

            where, C1 = Enc (pk, M1, Eval. );
and, for arbitrary M1 and M2 E M

·      A
homomorphic encryption model consists of several algorithmic parameters for
further clarification : KeyGen


  Implementation Of Partially Homomorphic
Encryption Scheme


·      The issue of security has hindered an open problem in cryptography,
more in the area of confidentiality and integrity security. Let us take
examples on how several schemes in conjunction with homomorphism deals with
these issues


Example I : RSA Homomorphism

Consider two large prime numbers p & q such that n=pq  and gcm(p,q)=1. Select a form

                j(n)=(p-1)(q-1) ‘ ab=1modj(n),
implying that a=(b inv.) mod j(n).

The parameters (n,b) are public while the three
parameters p ,q and a are private. Compute the encryptions:

Enc. K (x)
= x^b mod n = y where x is plaintext and y is ciphertext.

Dec. K (y)
= y^b mod n = x

The homomorphism of RSA is computed as

Consider two plaintexts x1 & x2. Then their
homomorphism is expressed as:

Enc. K
(x1) Enc. K (x2) = (x1^a) (x2^b) mod n

                                        = (x1.x2) ^ b mod n

                                        = Enc. K (x1*x2)

II : El Gamal Homomorphism

Randomly select a large odd number and a
generator a e Zp, select a &
b such that

b=a^a(mod p)

Make p, a and b public; Give a parameter r e Zp-1
also a secret random number. Then,

Enc. K
(x,r) = (a^r mod p, xb^r mod p)

The homomorphism of El Gamal Homomorphic
cryptosystem can be computed from its cryptosystem as follows:

For two arbitrarily plaintexts x1 & x2;

Enc. K
(x1,r) Enc. K (x2,r)  = (a^r1
mod p, x1 b^r1 mod p)( a^r2 mod p,                                               
                                               x2 b^r2 mod p)

                                             = (a^r1 a^r2 mod p, x1, b^r1
mod p)

                                             = (a^(r1+r2) mod p, (x1 +x2) b^r1+r2
mod p)

                                             = Enc. K (x1+x2, r1+r2)

homomorphic encryption scheme, the algorithm encrypts one bit at a time.
Therefore, in order to encrypt a binary string we will have to encrypt every
bit individually.

  Fully Homomorphic Encryption Scheme

Partial homomorphic encryption schemes had one
drawback, that they could only perform one binary operation at a time which
means they could only perform addition or multiplication computations at once.
On the other hand, fully homomorphic encryption can perform several addition
computations and one single multiplication computation on ciphertexts. Ideal
lattices and bootstrapping technique are employed to deliver this feature of

The major task of FHE is to improve the cloud
computing security. This operation takes place in a certain format given below:

First, the data is encrypted and sent to the
cloud computing environment;

Then, the encrypted data present in the
cloud is subjected to certain computations using some functions without
converting them into plaintext;

After querying the encrypted data, it is
sent back for being decrypted; and

Using the private key, the plaintext is
recovered from the computational out function.

Of Fully Homomorphic Encryption

No limited number as to how many manipulations
that can be executed on it;

The computational processes are performed on
circuit gates combinations of AND gates & XOR gates with ciphertexts as
their inputs;

FHE requires an additional algorithm in the
cloud named Evaluate algorithm which takes the public key, a circuit function f
and a tuple of ciphertexts which are used as inputs;

FHE can query a search engine without
revealing what is being search;

It is given by two models                                                                            
                                                         Decsk (C1
+ C2) = m1+m2 and Decsk (C1*C2) = m1*m2;

Compactness : This property demands that the polynomial function should not
increase in size. To put it simply, the ciphertext should remain bounded or
compact, independent of the function f;

Generally, ciphertexts contain some noise or
error in them. Employing homomorphic encryption on these ciphertexts drives the
ciphertext to render more noise. This in turn will make the decryption
infeasible. Besides, this also violates the ciphertext compactness property of


  Gentry’s Fully Homomorphic Encryption

This model of FHE consists of three steps:




The gentry’s FHE Computational process takes
place in the following way:

with somewhat homomorphic encryption : Somewhat homomorphic encryption  is limited to only a certain number of
additions and multiplications on ciphertexts. This limitation is a product of
the presence of noise in the ciphertext. If additional computations are applied
to the ciphertexts, the polynomial function increases in size and in turn will
render more noise and thereby resulting in infeasible decryption process as
once the noise reaches a certain level, the computational encrypted ciphertext
cannot be decrypted any further.

For instance, let noise be ‘n’ and the threshold
beyond which it causes uncertainties be ‘k’. Then, this bound is reached only
after log2 k levels of multiplication. In
SHE, the ciphertext cannot go beyond the bound as the message size gets larger
and this violates the ciphertext compactness property of FHE.

To achieve FHE, this enlarged noise issue must be
solved. Gentry’s method is used for ciphertext decryption but homomorphically
and have it sent to the cloud with the encrypted message. Such a private key
should have the capability to decrypt itself once in the cloud so as to decrypt
the encrypted message at some phases.

: This leads to a fully homomorphic encryption scheme. The fully
homomorphic encryption scheme goes through the following stages:

function which brings in a certain level of noise in the encryption scheme
that later generates compounded issues is
the centre of interest here

Every operation performed on the ciphertext
results in expanding the level of inherent noise;

The solution for this is to employ
bootstrapping for encryption;

Every time you re-encrypt, the noise is
subsequently cut down;

The operation of FHE is based on Ideal Lattices, that is ideal in number
theory which

Permits new complex circuit implementation;

Replicates the structure of ring

The FHE includes SHE as a part of it execution
operation. This is to not allow the algorithm to converge locally. The
bootstrapping noise algorithm for the SHE is idealized like this:

KEYGEN Evaluation : Output a random odd
integer p;

For bit m -> {0,1}, let m’ = m mod n; where m’ is even if
m=0, odd if m=1. Select a random q.

                        ENCRYPTE (m,p) = C = m’ +pq

M’ is the noise associated
with the plaintext

Let C’ = C mod p and C’ -> (-p/2,p/2)

Then, DECRYPT(p,c) = c’ mod 2

The noise encrypted C’ is considered to be the
noise associated with the ciphertext (that is, the shortest distance to a
multiple of p)

The homomorphism Multiplication:

Let m1,m2 -> {0,1} then,

Enc. e (m1,p)
Enc. e
(m2,p) = (m1′ + pq1)*(m2′ + pq2)

The decryption :

Dec. (c) =
(m1′ + pq1)(m1′ + pq2) mod p mod2

= m1’m2′ mod 2 = m1.m2

The compounding noise (m1′,m2′) results after a
certain number of operations after the loss of some homomorphic property.
Bootstrapping is introduces=d to stop the computational processed on the
inherent noise. This therefore allows the process to take in more inputs and
processes for a longer period and hence improves the performance.

With this contribution, the security of Big Data
has been improved significantly in the cloud computing environment.

  Application Of Fully Homomorphic Encryption
Scheme In Cloud Computing

In the implementation, analytical performance of
the FHE cryptosystem works on the virtual platform as a cloud server. The
transmission of the encrypted data to the virtual server which is the storing
caveat of the encrypted data is handled by a VPN. This is also the platform
over which the FHE processes are computed.



fig – Database server and
client implementing homomorphic encryption






fig – Implementation of FHE in
the cloud between the server and the client



This paper has aimed at Fully Homomorphic
Encryption Schemes along with some number theory and algebra, their usefulness
in the computational application of Big Data Security & Analytics in the
cloud. The common problem with cloud computing is the privacy and the
confidentiality of the crucial data of the client along with the computation of
the data stored in the cloud computing.

The issue was then resolved by sending the data
encrypted to the cloud in a uniform fashion. We must explicitly make the
assertion that other mechanisms exist for secure computation.

In cloud, usually different data providers are
required to exchange information such as the private key. FHE schemes being
public key schemes are best suited for this scenario as many sources of data

  Future Works

Homomorphic Encryption will foster in a new
dimension to the cloud storage. It provides security and confidentiality to the
data without its plain text being revealed in any stage.

This proposed algorithm is used in Amazon Web
Services after being further simplified and can be used in online auctioning,
medical and business purposes of the many more applications it has.

Although, more research needs to be done in this
process for reducing the size of the ciphertext for efficient data processing
so that more computations can be processed on it before it crosses the bound
beyond which it starts posing the inherent noise issue.

Furthermore, there is also a need to evolve
various algorithms for searching and querying on encrypted data under FHE



Z. Brakerski, C. Gentry, And V. Vaikuntanathan,”(Leveled) Fully
Homomorphic Encryption Without Bootstrapping,” In Itcs, 2012

D. Boneh, E. J In Goh, And K. Nissim, “Evaluating 2-Dnf Formulas
On Ciphertexts. In Theory Of Cryptography”; Conference, Tcc 2005, Volume 3378
of Lecture Notes in Computer Science, Pages, 325-341, Springer, 2005

W. Diffie, M. E Hellman, “New Directions in Cryptography, IEEE
Transactions on Information Theory, Volume IT-22, No. 6, (Online)

C. Gentry; “A Fully Homomorphic Encryption Scheme”

C. Gentry and S. Halevi, Implementing Gentry’s Fully Homomorphic
Encryption Scheme, Preliminary Report, (Online). 2009

C. Gentry, “Computing Arbitray Functions Data” Communication of
the ACM, V ol. 53, pp 97-105, 2010

S. Goldwasser and S. Micali: “Probabilistic Encryption & how
to play mental porker keeping secret all partial information”, In proceedings
of the 14th ACM symposium on the Theory of Computing (STOC ’82), New Y ork, NY
, USA, pp,21-53

Goldwasser, S. and S. Micali, ” Probabilistic encryption”, In
Journal of Computer and System Sciences, 1984, Vol 28, no. 2, pp270-299

J. Katz & L. Yehud, “Introduction to Modern Cryptography”,
Chapman& Hall/Crc Cryptography and Network Security, 2007.

K. McCaney, “New Encryption method with promises end-to-end cloud
security” http://gcn.com/Articles/2013/06/Encryption.end.to.end.cloud.
security.aspx?Page =1

J. Nittin, Saibal K. Pa and Dhananjay, K. Upadhyay;
“Implementation and Analysis of Homomorphic Encryption Schemes, International
Journal on Homomorphic Encryption

P. Paillier: Public-key cryptosystems based on composite degree
residuosity classes. In 18th Annual Eurocrypt Conference (EUROCRYPT’99),
Prague, Czech Republic, Volume 1592,1999

P.Meliand Timothy Grance, The NIST Definition of Cloud Computing,
Special Publication, 2011 800-145, Recommendation of the National Institute of Standards
and Technology. ?