App Permission Check Service for Parents

3.60

Derivation Chain

Step 1 GitHub Copilot malware

→

Step 2 Middle-aged digital security anxiety

→

Step 3 Parents' smartphone app excessive permission problem

→

Step 4 Diagnosis of installed app permission risk + cleanup guide

Problem

Parents in their 60s-70s have 30-50 apps of unknown origin installed on their smartphones and leave them with excessive permissions like contacts, location, camera, etc. Their children (40s-50s) worry, but only have chances to touch their parents' phones during holidays. Damage from small-amount payments due to malicious apps among those 60+ amounts to 120 billion KRW annually (approx. $90 million).

Solution

On the web, parents upload screenshots of their smartphone settings screen, and the service analyzes installed app list and permissions, displaying risk levels as red/yellow/green. For each risky app, provide specific actions in large text like 'Delete this app' or 'Turn off this permission'. Share result link with children.

Target: Ages 45-55, children of parents 60+, office workers concerned about parents' smartphone security.

Revenue Model: Free basic diagnosis (once a month). Premium (monthly automatic check report + KakaoTalk notification to parents) at 2,900 KRW/month (approx. $2.20)

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

4.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (69%)

Tech Complexity

29.3/40

Data Availability

19.4/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (51/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

7.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

5.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Frontend [low] Backend [medium] AI/ML [medium]

Dashboard