Differences Between Data Security And Data Privacy – A Critical Evaluation

Data models of the proposed system

Discuss about the Critically assess and evaluate the differences between Data Security and Data Privacy?

Green Rent Agency manages is an agency that manages properties for rent in South East London. The client that registers to this company provide their personal details and information about the type of property they want and the location. The agency provides details of the property to the client, and after the client agrees with the estimated rent, the agency finishes the agreement. Another company named Your House sells properties but does not deal with rentals. Recently the Green Share Agency wishes to integrate two companies into one large system, which will have two different sections in which one-section deals with the client that wants the property on rent and another section, which will deal with the selling of properties.

The Green Share Agency will have an online website through which the organization will maintain their different operational functions. One section of the website will deal with the selling of properties, and another part will have a system where the clients can manage properties on rent.  The User who wants to register with the company can freely choose between the two sections into which they want to register. The user can register to the website without spending money but if they want to upgrade their profile to premium level, where the users will be notified on a daily basis according to their search results. Also, premium members will have a special advantage of getting a 24X7 online help center.

The list of problems that user face is also included in this section and are briefly described point wise.

Sometimes user creates a portal on selling section but the website automatically updates the user’s profile and creates a profile into renting portal. By this user who wants to sell properties or buy properties also gets notification of renting properties.

The current searches on the portal are not so advanced as of now, and because of that user sometimes get mixed results irrespective of what they searched.

Helping center on the premium portals should be accessible by the members only. Sometimes queries sent by the client take more than enough time to get a proper answer.

This section expands the new merging concept of two companies into a single large company and describes it through system level. Both the conceptual model i.e. EERD and the logical model i.e. Mapped EERD are shown below for the better understanding of this report.

Conceptual Model (Proposed EERD)

For the proposed EERD the Chen’s notation is maintained throughout the section. A Bottom-up approach is taken for the EERD. The two companies Green Rent Agency and Your-House, which are under a large system Green Share Agency. Below the Extended Entity Relationship diagram of the proposed system is given.

Figure 1: Extended ERD of Green share Agency

(Source: Created by Author)

As it is seen from the above diagram, green rent agency and your house are two agencies which comes under the main organization green share agency. Green share agency has the attributes like company websites, company records and company ID. Again the agency green rent agency is connected with the client with the relationship registers. Client have attributes like client name, client address, client ID. Accordingly, the company your house is also in relationship with client named registers. Both the companies, green rent agency and your house have connected with an entity name property through different relations. Green rent agency manages properties for rent which is the relationship between green rent agency and property. On the other hand, your house is also in a relationship with customer which is manages sell of properties. Again your house charges 2.5% for a single agency and 3% for multiple agency. Property is an entity which is connected with both the companies and has attributes like property id, zone, land no, tax rate. A generalization is shown as the Green rent agency and your house are both under the same organization Green share agency.

The section contains the logical model of the proposed EERD. To convert from the conceptual model to logical model several steps are required. This section decomposes the conceptual model up to 3^rd normal form. First the steps to remove the anomalies are given below which are also the steps to convert from UN normalized form to normalized form. The steps are given below.

Figure 2: Steps to convert to 3NF

(Source: Created by Author)

Now, at First, the list of tables is identified from the above EERD, then the key constrains are listed. One row of each table is given as an example.

Company ID	Company website	Company records
CMP123	www.greenshare.com	HR policy

Table 1: Green Share Agency Table

Client ID	Client Name	Client Address
CL123	Harris Morrison	472/B Green Avenue B-street

Table 2: Client Table

Property ID	Zone	Land No.	State	Price	Tax rate
PR123	East zone	B456	ST1	700000 Lakh	15.6 %

Table 3: Property Table

To make the entire database anomaly free, property table is decomposed up to 3NF. At first, the functional dependencies are shown below.

Property ID -> {Zone, Land No., State, Price, Tax rate}

Logical Model (Mapped EERD)

{Zone, Land No.}-> {Property ID, State, Price, Tax rate}

Zone-> Tax rate

State-> Price

State-> Zone

Above the functional dependencies are shown. On the first bullet Zone, Land No., state, price and Tax rate are functionally dependent on Property ID, which is a primary key. Next again Property ID, state, price and tax rate are dependent on Zone and Land No., which can be combined to form a super key. In the same way Tax rate, Price and Zone are functionally dependent on Zone and State respectively.

Now the decomposition of the property table is shown below. The decomposition is done up to 3NF.

Figure 3: Decomposition of Property Table

(Source: Created by Author)

Now the key constraints used in this database are mentioned. Super key and primary key are the key constraints used for this whole system.

Super key: In the property table {Property ID, Land No.} is a super key.

Primary key: In every table there is one primary key present. For example, in the client table, Client ID is the primary key present.

Every database management system has a data dictionary file that contains all the entries for every table present in the current database. The data dictionary file is kept hidden from the database users to prevent them from accidentally destroying any data set for any table. For this assignment, the structure of the data dictionary is given. In the diagram below, it is shown how the data dictionary is centrally attached to several resources.

Figure 3: Data Dictionary

(Source: Created by Author)

In the above diagram how the data dictionary is attached to the whole interface of the system are shown. Considering the organization uses Oracle database, the data dictionary of the whole database must be attached to the system kernel of Oracle. Data dictionary is a central component of every database. Data Dictionary describes the database and its objects. Every entry for the table is kept in the data dictionary. Below the data dictionary for each of the table is shown.

P/F	Field Name	Data Type	Field Size	Description	Null/ Not Null
P	Company ID	Long integer	NA	Defines company id	Not Null
	Company Website	Text	30	Name of the website	Not null
	Company records	Text	20	Records	Not null

Table 4: Data Dictionary of Company Table

P/F	Filed Name	Data type	Field size	Description	Null/Not Null
P	Client ID	Long integer	NA	ID of the client	Not null
	Client Name	Varchar	20	Name of the client	Not null
	Client Address	Varchar	25	Address of the client	Not null

Table 5: Data Dictionary of Client Table

P/F	Field Name	Data type	Field Size	Description	Null/Not  Null
P	Property ID	Long integer	NA	ID of the property	Not null
	Zone	char	20	Zone of the property	Not null
	Land No.	integer	NA	Specified land no.	Not null
	Price	Integer	NA	Price of the property	Not null
	Tax rate	Integer	NA	Tax rate of the property	Not null

Table 6: Data Dictionary of property table

This section describes the major flaws in the database system and also gives a solution to evade those problems. As discussed above, there are several problems with the whole system. The major annoying problem which occurs is jumbling up between renting profile and selling profile. A user who created a profile for selling sections also sometimes get notified for the renting purposes. This is pretty annoying. The possible solution to this problem might me spending more time while designing the logic for the database and the whole system.

Data Dictionary of the proposed system

Another major problem with the database system is that database servers are expensive. With the industry growing the data models also grow big and then it becomes very difficult to store all the data in a single database server. A rudimentary and expensive solution to the problems is to go for a bigger database server by scaling up the budget. Another solution to this problem could be to establish a master-slave system where only the writing is done to the master system and but the user cannot read from all the slaves. This solution works by log shipping that means slave database will lag behind the master. This phenomenon is also known as Eventual consistency.

Another common problem that is identified is the connection error while logging into the system. Sometimes database connection errors are seen on the startup or login page. In such cases, the problems can be solved by directly logging into the RDMS system. The database can be connected by going into the database configuration settings.

Although the terms Data Privacy and data Security looks similar more or less but these are two different aspects. Data Privacy is a part of Data Security (Wang et al. 2013). For better understanding the differences between both the terms at first the definition of both the terms are given and then the differences are evaluated in the next paragraph.

Data Security: Data Security refers to the confidentiality of the data, and the topic is all about maintaining the integrity of the important information or sensitive data. In other words, Data Security refers to all practices that are made to ensure the integrity of the data and to make sure about the fact that authorized personnel are only accessing the authorized data. Now a day’s every company deal with lots of data for their work purpose on a daily basis. Whether the data is for public or private and confidential information about the company, it must be secured enough to maintain its integrity (Chen and Zhao 2012). Data security sometimes means necessary steps for the protection of important data, and that process includes even modification of important data and deletion of old unused data. A data security plan includes several processes such as fetching necessary information, keeping it safe until it is necessary for the business and atlatls destroying any information that is no longer needed. Mentioned steps will help any business maintain the data security.

Data Privacy: Data Privacy may be defined as the appropriate usage of data. Data privacy is the term that is interlinked with the industries. In industrial, level every organization deals with tons and tons of data on a regular basis. Data Privacy means a lot to the organizations that deal with important and context sensitive information.  Especially financial sectors that deal with customer’s information are much aware of Data Privacy (Sood 2012). Whenever a company or a merchant is using an entrusted data, then those data should be used according to the agreed policies. The Federal Trade Commission enforces several penalties against the organizations, which are neglecting this important issue about marinating data privacy of their customer. Data Privacy is also familiarly known as information policy, which is an important aspect of information technology (IT) that deals with the ability of an organization or ability of individuals to determine what data should be disclosed to the third parties and what data should be kept safe and secure from the other parties. All these are part of Data Privacy.

Relationship between Data Security and Data Privacy

Although both the terms Data Security and Data Policy are different respective of their meanings, there is a relationship between these two terms. Data Privacy is a part of Data Security. On an industrial level, companies need to maintain the Data Security through several security policies for maintaining the Data Privacy (Rewagad and Pawar 2013). With the improvement in process and technological field, there are several security policies developed for the protection of important and sensitive data from the outside world.

Difference between Data Security and Data Privacy

Data Security is all about marinating the integrity of the data by several steps such as sending data from one point to other point encryption of data by several encryption algorithms like hash, md5 and more (Goia and Vieu 2014). In today’s world, Data Security means, how much protected is data from outside network threats. The Data security is much needed in the organizations like financial and banking sectors.

On the other hand, Data Privacy is another term where the main concern is about protecting the sensitive information of target consumers from outsiders. Data Privacy is a common and important issue in industries. Business organizations need to protect their client’s personal details sensitively and for that, they develop several security policies that ensure the Data Privacy.

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