NEW TECHNOLOGY APPLICATIONS, DESIGN & BUSINESS MODELS
SUBMITTED TO :Prof. Rakesh Basant, Economics and CIEE (IIM) Prof. Deval Kartik, Strategic Design Management (NID) Prof. Bhavin Kothari, Strategic Design Management (NID) Prof. Jignesh Khakar, New Media Design, (NID)
SUBMITTED BY:- GROUP 14 Abhilash Narayan Eeshan Kumar Saha Mithilesh Sarode Shruti Sharma
Contents Description of idea ..................................................................................................................... 3 What is HideApp ........................................................................................................................ 3 Working of HideApp ............................................................................................................... 3 System Requirements ............................................................................................................ 4 Technological Model .................................................................................................................. 5 Precedent Study ..................................................................................................................... 5 Fingerprint Recognition Mechanism ...................................................................................... 6 Technology applied ................................................................................................................ 6 Technological Flow (Fingerprint Recognition with Embedded Cameras on Mobile Phones) 7 Data Collection ................................................................................................................... 7 Initialization ........................................................................................................................ 8 Enrolment ........................................................................................................................... 8 Comparison ............................................................................................................................ 8 Business Model .......................................................................................................................... 9 Market Size ............................................................................................................................. 9 Expected Growth .................................................................................................................... 9 Target Segment ...................................................................................................................... 9 Expected Target Segment Size ............................................................................................. 10 Revenue Model ........................................................................................................................ 11 Free Version: ........................................................................................................................ 11 Paid Apps .............................................................................................................................. 15 Sources of advertisement .................................................................................................... 15 Appendix .................................................................................................................................. 16 References ............................................................................................................................... 17
Description of idea The basic idea is to extend the finger recognition functionality of the cell phones to use fingerprints as passwords or keys for restricting certain files or apps in the cell phone. To maintain confidentiality of data in certain software/apps in the cell phone like mails, messages, documents, etc., generally people use passwords to lock them but passwords are not safe as they can easily broken or guessed. Fingerprints are a better lock as they cannot be faked easily. Right now finger prints are used to unlock the whole phone, but if you need to lend the phone to someone else, it becomes difficult to ensure confidentiality as the lock cannot be activated at that time. Here comes the use of HideApp
What is HideApp HideApp is a smartphone application which is aimed at helping users protect their private and confidential data. The app can lock not just an individual application like notes, pictures, messages, etc but also a combination of applications. This will enable the user to protect all the confidential data on their smartphones and ensure that even if the passwords or pattern locks can be broken by hackers, their confidential data will remain secure.
Working of HideApp The HideApp , when installed will use the root directory of the smartphone and generate a lit of all the applications installed on the phone. The list of installed apps will then be displayed to the user, where they can check the apps the want to lock using their fingerprints. The user will have the flexibility of changing the apps the want to lock/unlock. If a user does not wish to lock a particular application, he can uncheck it.
We also realise that the users do not want to always lock their apps, especially when they themselves are using it. Hence the users will have an option to switch on or switch off the HideApp whenever they want to. This will prevent unnecessary
procedures of fingerprint verification when the user himself/herself is using the smartphone.
System Requirements This application is aimed at smartphones having camera of 3.2 megapixels or above. Since most of the smartphones/tablets today have a camera of atleast 3.2 megapixel or higher, the app will be compatible with almost all the smartphones existing today, providing us with huge target market.
Technological Model Precedent Study a. Prior Art #1 – Fingerprint recognition with embedded camera in mobile phones. Fingerprint recognition has been used in many different applications where high security is required. A first step towards a novel biometric authentication approach applying cell phone Cameras capturing fingerprint images as biometric traits is proposed. The proposed method is evaluated using 1320 fingerprint images from each embedded capturing device. The overall results of this approach show a biometric performance with an Equal Error Rate (EER) of 4.5% by applying a commercial extractor/comparator and without any pre-processing on the images. http://www.derawi.com/cv/publications/derawi_final_mobisec.pdf b. Prior Art #2 - Face Recognition Software to Login to Windows Blink from Luxand is software that uses the webcam to read a face, match it against a pre-stored collection of users and log in the right account.Once the software is installed, the person just needs to configure it, which involves having Blink learn the face and remember it.Next time logging into Windows is attempted, Blink automatically enables the webcam and recognize the user’s face to log in with no passwords needed. http://www.addictivetips.com/windows-tips/face-recognition-software-to-loginwindows-7/ c. Prior Art #3– Fingerprint recognition Phone Pantech GI100 is a user-friendly security fingerprint recognition phone for using fingerprints for lock-in keys. It has a built-in 1.3 Megapixel camera with embedded flash. Only a registered fingerprint can enable keying in and use of the phone. http://www.gizmag.com/go/3084/ d. Prior Art #4 – ClassiEyesFingerprint recognition using mobile camera phone http://www.youtube.com/watch?v=mLx8nC1-Fpg
Fingerprint Recognition Mechanism The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns. The three basic patterns of fingerprint ridges are the arch, loop, and whorl: Arch: The ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the finger. Loop: The ridges enter from one side of a finger, form a curve, and then exit on that same side. Whorl: Ridges form circularly around a central point on the finger.
Arch Pattern
Loop Pattern
Whorl Pattern
Source:- (Fingerprint recognition) The major Minutia features of fingerprint ridges are: ridge ending, bifurcation, and short ridge (or dot). The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are ridges which are significantly shorter than the average ridge length on the fingerprint. Minutiae and patterns are very important in the analysis of fingerprints since no two fingers have been shown to be identical. It is through analysis of Minutia that accurate fingerprint analysis is possible.
Technology applied Considering the fact that these days mobile devices have become highly critical due to a huge amount of stored information in them like bank passwords, token numbers,
names, addresses, messages, notes, future plans, pictures, etc., the security of these devices becomes highly essential. The methods for authentication for ensuring security can be categorized into three classes: something you know (alphanumeric password), something you have (tokens) and something you are (biometric property). Unlike the other two, the third cannot be stolen, forgotten or broken. Supporting that nowadays almost all cell phones have embedded camera phones and most of them are above 3.2 mega-pixel in resolution, which facilitates a decent finger recognition with actual error rate of around 4.5%.
Technological Flow (Fingerprint Recognition with Embedded Cameras on Mobile Phones) The flow of processes and information can be clearly seen through the figure below:
Source:“Fingerprint Recognition with Embedded Cameras on Mobile Phones� by Derawi M., Bian Yang B., and Busch C., Norwegian Information Security Laboratory, Gjvik University College.
Data Collection Users enrol themselves to the system and their data is stored in the database. The features are then used for comparison later by the application. These are registered users and the enrolled data will be used for all calculations henceforth.
Initialization In this stage, the user presents his/her biometric characteristic to the sensor equipment. A picture of the fingerprint is captured by the camera which acts as a sensor. After the capture, the following steps are performed.
Pre-processing
Data Analysis
Feature Extraction: the minutia points
After the extraction, a database is established for comparisons in the future.
Enrolment The extracted data is stored in a database and linked to the user who initialized. The data stored in the database is the reference template for comparisons. It is a common approach to generate a single minutiae templateby deriving features from multiple captured samples.
Comparison Whenever a user logs into the system, the features of their fingerprints are compared with the existing database, based on the claimed identity. Based on the similarity, a score S is generated. A high S score means a high similarity and vice versa. In the next step, the similarity score S is compared to a predefined system threshold T and decision output is generated.
If S>T, user is genuine and is allowed to log in
If S<T, user is an imposter and is rejected by the system
Business Model Market Size Globally, Smartphone Market grew 61% in 2011 with total shipment volumes reaching 491.4 million units in 2011. Smartphones are replacing features phones at much faster rate than expected, thanks to consistently falling pricing. According to a recent report in the beginning of 2012, the estimated number of smartphone users stood at 1.08 billion. In comparison to Global Smartphone Market, growth in India is expected to be even higher. According to â&#x20AC;&#x153;India Smartphone Outlook for 2012â&#x20AC;? report released by Convergence Catalyst, India will witness 100% growth in 2012, with total smartphone shipments expected to reach 20 million units.
Expected Growth A report by Cisco has estimated that the Compounded annual growth rate of smartphone users in 2011-2016 to be 24 %. Going by this estimation, the number of users in smartphone users should grow to 3.2 billion by the year 2016. Also, the time spent per user on the smartphone is predicted to atleast double. The number of users connected to the internet through mobile is estimated to increase to over 80%.
According to ABI Research estimates, 29 billion apps were downloaded worldwide in 2011, up from nine billion in 2010, the market growing at 12% month-on-month. And according to International Data Corporation (IDC), the global mobile app downloads are forecast to soar to 182.7 billion in 2015.
Target Segment The app is more suited to active users of smartphones who aim to use the device to enhance their connectivity and productivity. These are the people who care most about the privacy of their data in their smartphones. Hence, our target segment predominantly is the corporate executives and young individuals who are highly conscious of the data in their cell phones.
The app would be made available on Apple store for iPhones, Google Play store for Android and other mobile brand specific stores.
Expected Target Segment Size Hence according to conservative estimates, the market for applications locking apps on smartphones is around 20 million currently. However, given the fact that the smartphones are increasingly becoming devices which contain a lot of confidential data and contacts, there is rapidly increasing awareness and need for privacy and protection of privacy. Hence we believe that the penetration of such category of apps would increase significantly.
Revenue Model The above data presents an exciting prospect for the development of smartphone apps and their monetisation. However, since we plan to launch the app soon, we will take into consideration the current usage data for building our revenue model.
Currently, the smartphone app market generates revenues mainly through 2 major sources 1. Advertisements embedded in apps 2. Purchase of paid apps by users.
According to research done by us, the top 4 free apps for locking various applications had combined downloads of above 10 million downloads. Compared to that, the top 3 paid apps for the same purpose had combined downloads of about 20,000. This clearly illustrates that the users who download apps are heavily biased towards free apps. Hence it is absolutely necessary to develop free versions of the app in order to generate high number of downloads.
Most of the app users prefer free apps atleast when they try it out, and very few are willing to buy an app they like. Hence we intend to have a two-fold revenue model: 1. Free Version - Revenue through advertisements 2. Paid Version - No advertisements, revenue through purchase of app by the users
Free Version: Majority of the users of smartphones are college students and young professionals who have a huge barrier to purchasing smartphone apps because of the plethora of free apps available. Hence, in order to popularise the app, it is absolutely necessary to have a free version of the application. Typically the user attention to the advertisements on mobile apps is very low, hence, the revenue per 1000 impressions is pretty low (approx $3). Hence, its vital to have high number of downloads to have the required revenue from advertising. Since people value privacy of data on their smartphones, a good app for ensuring the
privacy would be eagerly accepted. Hence we expect the app to have sizable number of downloads.
The chart below graphically depicts the revenue model for a typical smartphone app.
Target Users
No Downloads
Downloads
Converts
Paid Users
Revenue Through Purchase
Non-Converts
Free Users
Low advertising revenue
Without Data Connection
With Data Connection
No advertising revenue
Advertising Revenue
We have prepared a revenue estimate from advertising for a smartphone app. We have taken the following factors into consideration:a. The conversion of apps is low (approx 10%). Conversion implies the number of downloaders who frequently use the app, and do not delete it after initial use.
b. In India, the network connectivity, which is required for advertisements, is relatively low. A lot of users do not have GPRS/3G connection 24x7. That hinders the advertisement impressions made, while the app is being used, and hence reduces the revenue. c. Studies show, that a user uses app for relatively short period of time, before substituting it with another app. Hence, we assume that a user will use the app for an average period of 3 months before switching to another app.
According to our estimates, an app which has 100,000 downloads will generate $34,020 in its lifetime.
Downloads
100000
Conversion rate (active usage)
10%
Non Converts Non-Active users Network Connectivity Networked devices
Converts 90000 Active users
10000
60% Network Connectivity 54000 Networked devices Duration of using app
60% 6000 6 months
5 Avg Daily usage(mins) Number of minutes used(avg) Advertisements(per min)
2 Usage
900
540000 Advertisements(per min)
Views
Views
Standard revenues ($ per
Standard revenues ($ per
1000 views) Total revenue Cumulative revenue
10
2 10800000
3 1000 views)
3
1620 Total revenue
32400 34020
Source:-http://www.quora.com/How-much-ad-revenue-can-be-expected-per-100-000-downloadediPhone-iPad-apps
As discussed in the section on Business model:1. The number of smartphones and apps used is projected to increase rapidly. 2. The connectivity percentage will grow higher 3. Percentage of users downloading privacy protection apps is expected to increase
Hence keeping these factors in mind we have estimated the total revenue to be generated by the app if it is continued till 2016. We have taken the following assumptions:a. The percentage of smartphone users downloading and using privacy apps will increase to 20% (conservative estimate). b. We have considered the optimistic scenario that our app will capture 8% of the market for such locking apps. This is based on the facts that:i.
The new users will download our apps
ii.
Some existing users of app lockers will shift to our app
c. The market share of apps increases and decreases exponentially
We have built a conservative estimate which does not include potentially higher revenue due to:a. Increase in advertising revenue due to increase in number of impressions owing to increased smartphone usage b. Ability to command higher advertising revenues if the app becomes popular Column1 Smartphone users (Mn)
2011
2012
2013
2014
2015
2016
1080
1339
1661
2059
2553
3166
2%
5%
9%
14%
17%
20%
21.6
67.0
149.5
288.3
434.1
633.2
1%
5%
8%
7%
4%
2%
Percentage using Privacy apps Potential market Size Market Share Total Downloads('000s)
2,16,000
Revenue Per 100,000 ($)
34020
Total Revenue
33,48,000 1,19,56,378 2,01,79,708 1,73,62,786 1,26,64,620 34020
34020
34020
34020
34020
$73,483 $11,38,990
$40,67,560
$68,65,137
$59,06,820
$43,08,504
Paid Apps We have planned to launch the paid app after launching the free app. The paid app would be free from advertisements and we plan to add a few extra features and option to lock unlimited apps. Hence, we intend to decide the pricing later. However, our research has shown that most of the apps which fall into utility category are priced between $3-$8. Hence, depending on the popularity of our free app at the time of launch of paid app, and the additional functionality we offer in our Paid version, we will price the app accordingly.
Sources of advertisement For a new app like ours, which does not have any initial users, it is unlikely to find specific individual advertisers. However, there are multiple services which act as an interface between us (App developers) and the advertisers. These services select the advertisements to be displayed on an app and pay the developers for the number of impressions. We have found the following advertisement services to be popular and viable for android platform:a. Google AdLab b. Admob c. Adsense Similarly, for iStore, Apple has its own service for advertising in apps. Tying up with these services would provide us with the advantage of not having to focus on finding advertisements, allowing us to focus our attention on further improving the app or developing the paid version.
Appendix (Fingerprint Recognition with Embedded Cameras on Mobile Phones)
Feature Comparison can be done using Neurotechnology algorithm on the processed images. For each algorithm the error rates were determined based on a threshold separating genuine and impostor scores. The False Match Rate (FMR) and False None-Match Rate (FNMR) were calculated. The calculation of FMR and FNMR is done in the following way. We have collected N data samples from each of M participants, then we have calculated similarity scores between two samples, either stemming from one finger instance or from two different instances. A similarity score between two samples from the same source is called a genuine score, while an impostor score is the similarity score between two samples from different instances. Given our setting, we can have N * M data samples from which we can calculate the total number of NGen=M*N*(N-1)/2 different genuine scores and NImp=M*N*(M-1)*N/2. Given these sets of genuine and impostor scores we can calculate FMR and FNMR for any given threshold T as follows: FMR(T) = (Number of incorrectly accepted impostor images >=T)/Total number of impostor images FNMR(T) =(Number of incorrectly rejected genuine images < T)/Total number of genuine images From this, we can find the point where FNMR equals FMR, or in other words the Equal Error Rate (EER). This rate is very common used value which is being used to compare different systems against each other, and it roughly gives an idea of how well a system performs. The overall performance (cross comparison of all ten fingers) for Nokia N95 performs significantly better than the Desire. This is so because of various reasons. The Nokia was placed in fixed way on the holder while capturing. Furthermore, the Nokia was set to an internal close-up mode setting. This mode is ideal for capturing details of small objects within a distance between 10 and 60 cm. Here we had to ensure that the auto-focus always resulted in better quality images at a small distance when capturing the fingerprints, whereas the HTC was manually adjusted by the human operator. Thus, this means that the Nokia N95's auto-focus was performing slightly better than the HTC Desire.
References Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2011â&#x20AC;&#x201C;2016. (n.d.). Retrieved from http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c 11-520862.html Derawi M., B. Y. Fingerprint Recognition with Embedded Cameras on Mobile Phones. Norwegian Information Security Laboratory, Gjvik University College. Developers look to cash in on smartphone apps. (n.d.). Retrieved from http://www.livemint.com/2012/05/03191430/Developers-look-to-cash-in-on.html?atype=tp Eight ways to keep your smartphone safe. (n.d.). Retrieved from http://www.bullguard.com/bullguard-security-center/mobile-security/mobile-protectionresources/8-ways-to-keep-your-smartphone-safe.aspx Fingerprint recognition. (n.d.). Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Fingerprint_recognition Mobile phone theft increasing across the uk. (n.d.). Retrieved from http://www.insure4u.info/homeinsurance-mobile/mobile-phone-theft-increasing-across-the-uk.html SMARTPHONE USERS AROUND THE WORLD - STATISTICS AND FACTS. (n.d.). Retrieved from http://www.go-gulf.com/blog/smartphone www.quora.com. (n.d.). Retrieved from http://www.quora.com/How-much-ad-revenue-can-beexpected-per-100-000-downloaded-iPhone-iPad-apps