Enterprise Cloud Security with Outsourcing Computation

DOI : 10.17577/IJERTV1IS7153

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Enterprise Cloud Security with Outsourcing Computation

Amrit Mukherjee1, Aditya Sinha2

Consultant(Faculty)1,Head of Department2


The use of cloud computing is becoming widespread, but systematic study of its managerial implications is lacking. This paper examines cloud computing in the context of other major changes in Information Technology and explores the revolutionary transformations and challenges it brings to enterprises security . The paper also analyzes the Homographic encryption for enterprise clouds and its practical concerns regarding outsourcing computation; In this paper we have discussed the Actor model computations and its benefits for business oriented clouds and its security while considering outsourcing computing.

  1. Introduction

    For years the Internet has been represented on networks diagram by a Cloud symbol until 2008 when a variety of new services started to emerge that permitted computing resources to be accesed over internet termed Cloud computing. Cloud computing encompasses activities such as the use of social networking sites and other forms of interpersonal computing; however, most of the time cloud computing is concerned with accessing online software applications, data storage and processing power. Cloud computing is a way to increase the capacity or add capabilities dynamically without investing in new infrastructure, training new personnel, or licensing new software. It extends Information Technologys (IT) existing capabilities. In the last few years, cloud computing has grown from being a promising business concept to one of the fast growing segments of the IT industry. But as more and more information on individuals and companies are placed in the cloud, concerns are beginning to grow about just how safe an

    environment it is. Despite of all the hype surrounding the cloud, customers are still reluctant to deploy their business in the cloud. Security issues in cloud computing has played a major role in slowing down its acceptance, in fact security ranked first as the greatest

    challenge issue of cloud computing as depicted in figure 1.

    Figure 1.Results of IDC survey rankings security challenges, 2008

    Cloud computing is currently characterized by having an on demand access to elastic resources via a tenancy model. It typifies the holy grail of no-worries in computing, allowing a company to focus on its core business, paying for all its IT resources as a service.

  2. Cloud computing service models

    In cloud computing, everything is delivered as a Service (XaaS), from testing and security, to collaboration and metamodeling [1]. The cloud was rapidly becoming a conflagration of buzzwords as a service. Today there are three main service models, which are agreed on and defined in the NIST document [2].

    Software as a Service (SaaS)

    this simply means delivering software over the Internet. It is the most widely known model of cloud computing. SaaS has been around since early 2001 when it was

    commonly referred to as the Application Service Provider (ASP) Model [8]. Software as a Service consists of software running on the providers cloud infrastructure, delivered to (multiple) clients (ondemand) via a thin client (e.g. browser) over the Internet.Typical examples are Google Docs and Salesforce.com

    Platform as a service(Paas)

    this gives a client (developer) the flexibility to build (develop, test and deploy) applications on the providers platform (API, storage and infrastructure). PaaS stakeholders include the PaaS hoster who provides the infrastructure (servers etc), the PaaS provider who provides the development tools and platform and the PaaS user [3]. Examples of PaaS are Microsoft Azure and Google AppEngine.

    Infrastructure as a Service (IaaS)

    rather than buy servers and build a datacenter from ground up, and consequently having to worry about what happens when the website hits a million users, IaaS offers users elastic on demand access to resources (networking, servers and storage), which could be accessed via a service API. The underlying infrastructure is transparent to the end user, while s/he retains control over the platform and software running on the infrastructure. IaaS runs on a tenancy model, which employs a usage-based payment approach allowing users to pay for only those resources they actually use.

  3. Cloud computng Characterisics

    This cloud model promotes availability and is composed of five essential characteristics. It is a model for enabling convenient on demand network access to a shared pool of configurable computing and outsourcing computing with respect to enterprises.

    On-Demand Self service

    The client has provision capabilities, such as Server time and network storage, as needed automatically without requiring manual interaction with each services provider.

    Broad network access

    Resource pooling

    The providers computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, network bandwidth, and virtual machines.

    Rapid elasticity

    Capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.

    Meausured Service

    Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.

  4. Cloud Deployment Models

    Capabilities are available over the network and

    accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms

    Figure 2.Deployment Models for Corporate and Federal Agencies

    Private cloud

    The cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on premise or off premise.

    Community cloud

    The cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on premise or off premise.

    Public cloud

    The cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

    Hybrid cloud

    The cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).

    Figure 3.Cloud with all models

  5. Cloud Growth in outsourcing computation Cloud computing is a combination of several key technologies that have evolve and matured over the

    years. This evolution to present day cloud computing

    includes a combination of open APIs, storage, computing, infrastructure. A service provider (such as Google or Amazon) provides hardware and virtualization software, and sometimes also applications. Instead of us, as users, hosting our own servers, their computers run virtual machines where our server can reside. The service providers machine has a certain type of software that does virtualization (VM ware), so that a single machine in the provider can run many virtual machines, each with own operating system, hard drives, and application software. If virtualization were done only through software, it would be very slow, so chip and CPU manufacturers like INTEL and AMD embedded in their chip set hardware that allows virtualization support to create virtual machines. To avoid the security problems of virtual machines, manufacturers such as INTEL make the chip CPU with a special code so that when we, as users, log into the virtual machine in the providers physical machine, we can be sure that the host machine is indeed ours, and not accessible to others without our permission.

    The Profitability of cloud computing can be explained in the "cost associativity" formulae shown in Formula 1., the left-hand side multiplies the net revenue per userhour by the number of user-hours, giving the expected profit from using cloud computing while the right-hand side performs the same calculation for a fixed-capacity datacenter by factoring in the average utilization, including nonpeak workloads, of the datacenter; whichever side is greater represents the opportunity for higher profit" (Armbrust et al., 2009). Ambrust et al. (2009, p. 10-11) gave example on elasticity with calculations on the potentials of cloud computing savings and cost reduction:

    UserHourscloud×(revenue- costcloud)UserHoursdatacenter×(revenue- (costdatacenter/utilization))

    Cost minimization of cloud computing to enterprises can be explained in cloud computing elasticity capability. Assume our service has a predictable daily demand where the peak requires 500 servers at noon but the trough requires only 100 servers at midnight, as shown in the figure 4.

    Figure 4.Provisioning for peak load and underprovisiong (Armbrust et al., 2009 p. 11)

    As long as the average utilization over a whole day is 300 servers, the actual utilization over the whole day (shaded area under the curve) is 300 x 24 = 7200 server-hours; but since we must provision to the peak of 500 servers, we pay for 500 x 24 = 12000 serverhours, a factor of 1.7 more than what is needed. Therefore, as long as the pay-as-you-go cost per server- hour over 3 years is less than 1.7 times the cost of buying the server, we can save money using utility computing (Armbrust et al., 2009 p. 10).

    In Figure 4,we observed: (a) Even if peak load can be correctly anticipated, without elasticity we waste resources (shaded area) during nonpeak times. (b) Underprovisioning case 1: potential revenue from users not served (shaded area) is sacrificed. (c) Underprovisioning case 2: some users desert the site permanently after experiencing poor service; this attrition and possible negative press result in a permanent loss of a portion of the revenue stream (Armbrust et al., 2009).

  6. Enterprise security challenges

    Cloud computing faces just as much security threats that are currently found in the existing computing platforms, networks, intranets, internets in enterprises. These threats, risk vulnerabilities come in various forms. The Cloud Security Alliance (Cloud Computing Alliance, 2010) did a research on the threats facing

    cloud computing and it identified the following seven major threats:

    Abuse and Nefarious Use of Cloud Computing

    Insecure Application Programming Interfaces

    Malicious Insiders

    Shared Technology Vulnerabilities Data Loss/Leakage

    Account, Service & Traffic Hijacking Unknown Risk Profile

    Moving to the cloud presents the enterprise with a number of risks and that include securing critical information like the protection of intellectual property, trade secrets, personally identifiable information that could fall into the wrong hands. Making sensitive information available on the internet requires a considerable investment in security controls and monitoring of access to the contents. In the cloud environment, the enterprise may have little or no visibility to storage and backup processes and little or no physical access to storage devices by the cloud computing provider. And, because the data from multiple customers may be stored in a single repository, forensic inspection of the storage media and a proper understanding of file access and deletion will be a significant challenge (Information Security Magazine, 2009).

    The researchers went on to say that a way around the weakness they found in Amazon's EC2 is for customers to insist that their cloud machines are placed on physical machines that only they can access or that they and trusted third parties can access (Greene, 2009). This solution will likely be at a price premium because part of the economy of cloud services is maximizing use of physical servers by efficiently loading them up with cloud machines (Greene, 2009) and locating the cloud datacenter where the utility price is the cheapest.

    Because of privacy concerns, enterprises running clouds collecting data have felt increasing pressure to anonymize their data. EPIC has called for Gmail, Google Docs, Google Calendar, and the company's other Web applications to be shut down until appropriate privacy guards are in place [7]. Google and

    Yahoo!, because of pressure from privacy advocates, now have an 18 month retention policy for their search data, after which it will be anonymized. This means that some identifying data will be removed such as IP addresses and cookie information. The anonymized data is retained though, to support the continual testing of their algorithms. Another reason to anonymize data is to share data with other parties. These may be to support research (e.g., the AOL incident [6]) or to subcontract out data mining on the data (e.g., the Netflix data set [6]).

    An example of indirect data-mining that might be performed by a cloud provider is to note transactional and relationship information (see World Privacy Forum Report [5]). For example, the sharing of information by two companies may signal a merger is under consideration.

    Availability also needs to be considered in the context of an adversary whose goals are simply to sabotage activities. Increasingly, such adversaries are becoming realistic as political conflict is taken onto the web, and as the recent cyber attacks on Lithuania confirm [6]. The damages are not only related to the losses of productivity, but extend to losses due to the degraded trust in the infrastructure, and potentially costly backup measures. The cloud computing model encourages single points of failure. It is therefore important to develop methods for sustained availability (in the context of attack), and for recovery from attack. The latter could operate on the basis of minimization of losses, required service levels, or similar measures.

    The development of cloud computing may, in the extreme, allow the use of thin clients on the client side. Rather than a license purchased and software installation on the client side, users will authenticate in order to be able to use a cloud application. There are some advantages in such a model, such as making software piracy more difficult and giving the ability to centralize monitoring. The development has potential security implications, both in terms of data leaks, and in terms of the number of sources of data a user may have to pull data from this, in turn, places requirements on how access is authorized for reasons of usability. While centralized access control may solve many of these problems, that may not be possible or even desirable.

    One examle in this area is provided by Facebook. Facebook users upload both sensitive and non-sensitive data. This data is both utilized by Facebook to present the data to other users, and also utilized by third party applications that are run by the platform. These applications are typically not verified by Facebook. Hence, there is a drive to create malicious applications that run in Facebooks cloud to steal sensitive data, e.g., see [4]. Furthermore, Abadi pointed out that it is hard to maintain ACID (atomicity, consistency, isolation, durability) properties of during data replication over large geographic zones. Data remembrance or persistence remains an issue due to replication and distribution of data even after a user has left a cloud provider.

    Figure 5.Data security life cycle

    Data in the cloud is usually globally distributed which raises concerns about jurisdiction, data exposure and privacy. Pearson [7] summarized the main privacy issues of cloud computing. Users are made to give away their personal information without knowing where it is stored or what future purpose it might serve. Organizations stand a risk of not complying with government policies as would be explained further while the cloud vendors who expose sensitive information risk legal liability. Virtual co-tenancy of sensitive and non-sensitive data on the same host also carries its own potential risks.

  7. Aplying homomorphic in outsourcing computation

What if the user could carry out any arbitrary computation on the hosted data without the cloud

provider learning about the users data – computation is done on encrypted data without prior decryption. This is the promise of homomorphic encryption schemes which allow the transformation of ciphertexts C(m) of message m, to ciphertexts C(f(m)) of a computation/function of message m, without disclosing the message. The idea was first suggested by Rivest, Adleman and Dertouzos in 1978, referred to as privacy homomorphisms [13]. RSA (invented by Rivest, Shamir and Adleman 78) [15] had multiplicative homomorphism (you could compute a ciphertext which is the product of plaintexts) and over the next 30 years, researchers such as A.Shamir [15],ElGamal 85 [14] and Paillier [13] came up with partially homomorphic cryptosystems. A survey of homomorphic encryption schemes can be found in [13,15].

An encryption scheme can be said to be fully homomorphic if:

E(m1m2)E(m1) E( m2);for all m1 , m2 M (1)

Where M is the set of plaintexts, – represents any arbitrary function and means computation is done without the plaintexts being decrypted. The first fully homomorphic encryption system was proposed by Craig Gentry in 2009 [16] using ideal lattices. Gentrys approach employed devising a somewhat homomorphic scheme, and then bootstrapping it to get a fully homomorphic scheme. Since then researchers have proposed variants and improvements to Gentrys model.

Smart and Vercauteren [19] presented a specialization of Gentrys scheme which yielded a smaller ciphertext size. Dijk, Gentry, Halevi, and Vaikuntanathan [17] introduced the first variant of Gentrys using arithmetic operations over integers. Stehle and Steinfield [18] also proposed a faster improvement of Gentrys model.

GENTRYS fully Homomorphic Encryption using ideal lattices:

An encryption scheme has the following three step algorithm:

  1. KeyGen – creates two keys i.e. the secret key sk and the public key pk.

  2. Encrypt – encrypts the plaintext m with the public key pk to yield ciphertext c.

  3. Decrypt – decrypts the ciphertext c with the secret key sk to retrieve the plaintext m.

    Gentry introduced a fourth step called Eval to the algorithm:

  4. Eval – outputs a ciphertext c of f(m) such that

Decrypt (sk,m) = f(m).

The scheme becomes homomorphic if f can be any arbitrary function, and the resulting ciphertext of Eval is compact (i.e. it does not grow too large regardless of the complexity of function f). The Eval algorithm in essence means that the scheme can evaluate its own decryption algorithm (i.e. the scheme is bootstrappable).

It can be shown that any arbitrary function is made up of an aggregate of addition, subtraction and multiplication functions (i.e. AND, OR and NOT gates). Gentry employed ideal lattices which provide additive and multiplicative homomorphism and low circuit complexities (for the decryption algorithm) in creating his fully homomorphic scheme. More on lattice based cryptography can be found in [5,6]. Further, in order to prevent the ciphertext (as well as the inherent noise/error) from becoming too large, Gentry introduced a Recrypt function which refreshes a ciphertext c to produce a new ciphertext c using a different key. Recrypt is a two-step procedure:

  1. Encrypt (pk2, c1) to yield c1, then

  2. output a new ciphertext c by Eval (pk2, Decrypt , sk2, c1).

This is the process behind bootstrapping to yield the fully homomorphic encryption. The main issue with this scheme above is the ciphertext and noise growth (especially w.r.t. function complexity).

  1. Implementing actor model computation for enterprise

    An Actor is a computational entity that, in response to a message it receives, can concurrently:

    send messages to other Actors;

    create new Actors;

    designate how to handle the next message it receives.

    There is no assumed order to the above actions and they could be carried out concurrently. In addition two messages sent concurrently can arrive in either order. Decoupling the sender from the communications it sends was a fundamental advance of the Actor model enabling asynchronous communication and control structures as patterns of passing messages [Hewitt 1977].

    An Actor can only communicate another Actor to which it has an address. Addresses can be implemented in a variety of ways:

    direct physical attachment memory or disk addresses network addresses

    email addresses

    The Actor Model differs from its predecessors and most current models of computation in that the Actor model assumes the following:

    • Concurrent execution in processing a message.

    • The following are not required by an Actor: a thread, a mailbox, a message queue, its own operating system process, etc.

    • Message passing has the same overhead as looping and procedure calling.

    Implementing Actor model can give intelligence for the cloud where the information is stored or to be act upon some operation like EC2 (Elastic Cloud Computing) and for the service. Here the noise which was earlier with Homomorphic encryption can be avoided as the Actors acts on each other rather than acting directly with the information.

    Computational Representational Theorem: The Computational Representation Theorem [Clinger 1981; Hewitt 2006] characterizes computation for systems which are closed in the sense that they do not receive communications from outside. The denotation DenoteS of a closed system S represents all the possible behaviors of S as

    DenoteS = Limiti Progression i



    S s

    where ProgressionS takes a set of partial behaviors to their next stage, i.e., Progression i4 Progression i+1 .In this way, S can be mathematically characterized in terms of all its possible behaviors (including those involving unbounded nondeterminism).Acting the Actor models in the source and in the packets which are communicating can be secured by multi-stage progressions.

  2. Conclusion

    Cloud is in warning from the perceived loss of control of sensitive data. Current control measures do not adequately address cloud computings third-party data storage and processing needs. In our vision, we propose to extend control measures from the enterprise into the cloud through the use of Trusted Computing and applied cryptographic techniques and by computational theorem. These measures hould alleviate much of todays fear of cloud computing, and we can, have the potential to provide demonstrable business intelligence advantages to cloud participation. Although and always, Enterprise should verify and understand cloud security, carefully analyze the security issues involved and plan for ways to resolve it before implementing the technology. For more security, Pilot projects should be setup and good governance should be put in place to effectively deal with security issues and concerns. Namely, the new threats require new constructions to maintain and improve security. Among these are tools to control and understand privacy leaks, perform authentication, and guarantee availability in the face of cloud denial-of-service attacks. Encryption in Homomorphic is although suitable for large informational clouds (Enterprise) where information intelligence is highly required for safer connections and usage.

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