B

SNS Privacy Exposure Analyzer

2.95

Derivation Chain

Step 1 LLM-powered large-scale de-anonymization technology
→
Step 2 Unintentional personal information exposure risk on social media
→
Step 3 Comprehensive privacy score calculation from SNS posts

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Active social media users in their 20s-30s (working professionals posting on Instagram, Twitter, and blogs) are unaware that LLM-based aggregate analysis of their public posts can infer their residential area, workplace, commute route, family relationships, and income level. While individual posts appear harmless, collective analysis of hundreds of posts can build an identity-level profile. This increases the risk of stalking, phishing, and social engineering attacks, yet no self-assessment tool exists.

Solution

(1) Analyze user's public SNS accounts (Instagram, Twitter, blogs) and generate a Report of inferable personal information (residence, workplace, family, schedule patterns, etc.), (2) Display risk scores per category and show 'which posts contributed to this inference,' (3) Recommend deletion or privacy setting changes for high-risk posts.

Target: Active SNS users (working professionals ages 20-35), job seekers (who need to manage public profiles), influencer managers
Revenue Model: Basic assessment free (top 5 risk items), full Report at 19,000 KRW (~$14), Monthly Subscription with weekly auto-scan at 39,000 KRW/month (~$29)
Ecosystem Role: Consumer
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
3.0/5
V Validation
2.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
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/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (50/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

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