LinkedIn can improve data classification and privacy
place
In
this report we will show how LinkedIn can improve data classification and
privacy place for their users, members, recruiters that regulate how various of
data are handled safely with an emerging technology.
Artificial
Intelligence has been improved a lot in recent years such as algorithms, big
data, and computing power. Here in this study, we will be introducing a
technology called OS-ELM (Online
sequential extreme learning machine) learning method in social
networks for node classification) for data classification improvement in
LinkedIn platform. Use of OS-ELM is more accurate and extremely faster
algorithm in comparison to other AI technologies OS-ELM “can learn data one by
one or chunk by chunk, with fixed or variable chunk sizes” and it will sort in
to relevant categories.
According to cyber news Data scraping is a huge risk
for LinkedIn and investing OS-ELM technology, LinkedIn can
protect users sensitive data such as full names, email addresses, phone numbers, workplace
information, and many more.
By implementing OS-ELM method will benefit LinkedIn huge cost reduction by 40% and the projected cost to implement this technology would be $4,000, 000. If LinkedIn cannot implement this technology they can be heavily fined for mishandling customers sensitive data and it’s not good for the LinkedIn brand and reputation.
Use the above introduction and complete the following.
Analyse two alternatives (~800 words):
(The two alternatives could be:
Option 1: LinkedIn hire some experts and develop the technology themselves.
Option 2: LinkedIn can find another firm who is already and expert in the technology and hire them to design the platform.
Another option could be LinkedIn decided to do nothing and keep the existing platform.)
For each alternative carry out detailed critical analysis for the following areas.
OFeasibility: (can be a little bit technical) – talk about personnel (R&D), how long the project can be, how technology/IT assets can be obtained
O Financial evaluation (talk about assumptions, provide a table similar as below)
OBenefits
OCosts (broad-brush figures, but understanding of different types of costs are important)
ORisks (risk register and matrix)
USA 
India