SWIPECrypto and the way Knowledge Privateness Panorama will Change Endlessly (Half 1)


After finisihing one long series about Blockchain the other day, it happens that the respond from all readers is quite good, and lots of them reach out to me through whatsapp or instagram (yep in Indonesia we use Instagram to ask serious question actually :P) to learn more about the nitty gritty of this technology.

Even though the question is still mainly revolving around investment (is it now too late to buy Bitcoin? Are Bitcoin buyers really become Billionares? When Moon?) but I take this as a sign of increasing public awareness of Bitcoin and Blockchain Technology.

me, whenever someone asking about Blockchain

For this essay, i will try to elaborate on of the exciting project that build on top of a Blockchain Technology.

My hope is by elaborating and try to describe an ongoing project, will give a clearer picture to my reader about what is the real use case of Blockchain Technology, what is exactly the benefit for user, and what is the differentiation between an existing centralized system with the decentralized one.

Project that i will review here is SWIPECrypto ( or SWIPE in short.

There’s a lot of things to be unpack, so let’s dig deep one by one.

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First thing first.

What is SWIPE?

Developer Apps that enable both apps developer and users to do a fair and transparent data monetization.

Developer Tools in a simple definition is a software that used by Apps Developer to improving capability of their application product and services.

SWIPE providing SDK (Software Development Kit) that can be installed instantly by Apps Developer that will grant them full access to SWIPE Ecosystem.

— — — —


Who are the parties that got involved in the SWIPE Ecosystem? For who is exactly SWIPE created?


Someone that create Mobile Application


Someone that using Mobile Application


Party who buys data from Apps Developer.

This can be a marketing agency, a research company, a FMCG Company, or big corporation who needs the data insight of a Application User to improve the effectiveness and efficiency of their marketing activity.

The Data that mention here is a user berhavior data or what kind of activity that the User do in the application, and not (hopefully) user personal data.

— — — —


How exactly the current sytem of data monetization works and why it needed a significant improvement?

from SWIPE Website

note : to make it easy for reader to undertand the process, we will user personification (name giving) for every party that got involved on the process.

a) the Application that we’ll discussed is called KOMPOS, Mobile Apps News — Reading Application that enable user to get the latest news for both local and international.

b) Apps Developer is named John, a smart Mobile Apps Developer from Stanferd University.

c) Apps user is named Alice, a young women who have a job as a cashier in local mini market store called Indomart, she is keen on reading political and showbiz news.

d) The Data Buyer is MARKON, a marketing research company that focusing on serving political consultant as their client.

The current data monetization process, from application building until the usage of the data, goes more or less like this : (note -there will be a simplification to enable all readers understand this)

  1. John create KOMPOS Mobile Application
  2. Alice, while updating the existing application on her smartphone, see that KOMPOS is now trending on Google Play Store, then decided to download it.
  3. The application get into Alice’s phone.
    This is the first phase of Data Giveaway, where Alice giving her Smartphone Device ID to John.
    Device ID is an unique identity that owned by each smartphone.
    There’s no two or more smartphone with the same Device ID.
  4. Alice required to fill her Private Data for KOMPOS Sign Up.
    Because she is reluctant to fill from the start, she decided to use Sign In using Facebook Account.
    This is the second phase of Data Giveaway, where Alice giving her full name, email, and birth date.
  5. Alice using KOMPOS to read news about politics, economy, sports, and various topics. Her favorite is politics and showbiz.
    This is the third phase of Data Giveaway, where Alice, without knowing, giving John the knowledge to her preference, from her favorite politician (reflected by how often Alice clicking on news that involving certain politician), what kind of news format does she enjoy best (long or short), what kind of topics (politics and showbiz), and what time she prefer to read the news and how long is the duration of each reading session.
  6. John create a user persona of Alice which is a compilation of personal data and Alice’s in-apps user behavior.
  7. Beside Alice, John has millions of other users using KOMPOS, so John have a big enough data bank that he can sell to the willing Data Buyer.
  8. MARKPLAS, which is an active data buyer comes and offer some money to buy KOMPOS Users data that has been through anonymity processto protect User Personal Data.
    The only personal data that John doesn’t hide is Device ID.
  9. MARKPLAS then do hyper-detailed user persona through combining data that has been acquired from KOMPOS with other application data that has also been acquired by MARKPLAS.
    Because Alice not only using KOMPOS in her phone, so there’s a lot of other user behavior which belong to Alice that in possession of others apps developers.
    For the sake of simplicity, let’s say MARKPLAS has acquired the data from Mobile Legenda (mobile game), Go-Jok (on-demand motor taxi services), three of the top e commerce application : Tokekpedia, Shopo, and Bukalapuk, and also two of the most well-known music streaming services : Spotifa and Joki.
    Same with John, every other Apps Developer giving the data that has been through anonymity process, except for the Device ID

Why is that Device ID is an ideal data anchor for apps user behavior data?

Below is a very comprehensive explanation from of what is Device ID and why it is important.

After we understand how important is our Smartphone Device ID, let’s examine an illustrated spreadsheet that owned by MARKPLAS as an active data buyer of several mobile application.
(note : all example here is using Indonesia Context and also the amount of money is in IDR, Indonesia Native Currency)

As you can see from the illustration that by combining several data points from several application beside KOMPOS, enabling MARKPLAS to get a deep insight into User Behavior for every Device ID that they have.

Let’s zoom into Alice profile (row 2, Device ID G2647B) to see what kind of insight MARKPLAS can learn from their Database Ledger.

With that much of data points, MARKPLAS can make a prediction based on insight about Alice :

  1. Alice play Mobil Legenda, a game application, on 7.30 in the morning, with an average duration of 2 hours, which reflect that she doesn’t have a typical office hour (which usually start from 8am) this give indication, that she is either of this one : freelance, entrepreneur, or employee with noon or night shift, or unemployed.
  2. Alice is not using Go-Jok (on demand motor-taxi application), which means either her mobility is not too high, or she is prefer to use private vehicle, either cars or motorcycle.
  3. Alice read KOMPOS only at night and only for 5 mins, her favorite topic is Showbiz, which consists entertainment news that majority containing gossips.
    This reflect that Alice doesn’t give a deeper attention to expand her knowledge base on serious matters.
  4. Alice’s Average Basket Size is only 75K idr, which is below standard for e commerce user. this, along with others data points, reflect that is a good chance that Alice is belong on C to D Economy Class.
  5. Her music preference is Dangdut (Indonesia Traditional Music), that is listened through free streaming application JOKI. this give affirmation to previous prediction that Alice is on C to D Economy Class.
  6. Ads Banner that is frequently clicked by Alice is a click bait banner with a sensational title such as “Want to Get Rich Quick? This is the Secret!
  7. Her favorite politician is Jokowi (Indonesia current president), so there is a big probability that she will prefer a politician with strong affiliation with Jokowi, whether he/she is the minister in Jokowi Administration or a member of Jokowi Coalition Party.

With this kind of insight and persona, MARKPLAS can do user categorizing, that Alice (G2547B on MARKPLAS side) is best suited into lower — affluent user category, with low economy class (C-D) and mid to low education level.

And from that categorization, MARKPLAS able to recommend an effective campaign content material for this user category, which is a simple, easy to understand, and with a fairly sensational headline.

MARKPLAS, too, can recommed an effective marketing channel application, to target this user category, which is Mobile Games, where they spent their longest time on.

MARKPLAS will license this refined data and insights to Final Data Buyer, which is their direct client, the Political Consultant.

Armed with this kind of deep insight into the user persona, political consultant able to manage a very effective and cost-efficient campaign, that used hyper-targeted political ads directly into Alice Phone (because remember the Ledger consist of Device ID)

Alice will see that the political ads with a relevant message popping right when she has the best attention span.

With the possession of Device ID, without having access to user private data (name, email, and phone number), apps developer able to do targeted campaign according to advertisement buyer, on this illustration, the Political Consultant.

Because the Device ID that belong to apps developer and political consultant is identical, they only have to do Device ID Matching. whether the Device ID targeted by the Political Consusltant is on the Apps Developer Database or not.

So What The Heck is Wrong with This?

Alice along with millions other KOMPOS user never know that all of this ever happen 🙂

That their user data behavior is being sold by John, the apps developer to MARKPLAS, which in turn, after some insight learning, sold again to final Data Buyer, the Political Consultant.

What Mba Sari knows, is that, all of sudden political ads with a sensational headline is now popping on her KOMPOS application, then reappear again when she is playing Mobil Legenda, her favorite mobile game.

All monetization data process is happen behind a closed door, without any control of knowing from Apps User.

SWIPE Independent Research team finds out that 98% of Apps User doesn’t have any awareness that the data they produce while using the application having any Monetary Value.

This is the problem that SWIPE, as Blockchain Project try to solve.

How Apps Developer able to do a fair and transparent data monetizationboth for Apps User and Data Buyer.

Transparent in a sense Apps User ought to know when their data is being sold by Apps Developer.

Fair in a sense that Apps User ought to get compensation for data that they contribute to Apps Developer.

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On the next part, i will try to elaborate three important things

  1. What is the Solution that offered by SWIPE to fixing this problem?
  2. Why SWIPE have to apply Blockchain Technology, including produce their own native token to solve this problem?
  3. What is the lesson learned from SWIPE, if, sometimes in the future we want to build decentralized application utilizing Blockchain Technology.

See you guys on the next part!

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