RRepoGEO

REPOGEO REPORT · LITE

data61/MP-SPDZ

Default branch master · commit 3a0a7b1d · scanned 6/24/2026, 2:32:05 PM

GitHub: 1,159 stars · 363 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
87 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface data61/MP-SPDZ, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumhomepage#1
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://mp-spdz.readthedocs.io/en/latest/
  • mediumreadme#2
    Clarify the repository's license(s) in the README

    Why:

    COPY-PASTE FIX
    Add a section to the README clarifying the specific license(s) that apply to the project, referring to the existing LICENSE file for full details.
  • lowreadme#3
    Emphasize 'framework' in the README's opening sentence

    Why:

    CURRENT
    This is a software to benchmark various secure multi-party computation (MPC) protocols in a variety of security models such as honest and dishonest majority, semi-honest/passive and malicious/active corruption.
    COPY-PASTE FIX
    MP-SPDZ is a versatile framework designed to benchmark and implement various secure multi-party computation (MPC) protocols across a wide range of security models, including honest and dishonest majority, semi-honest/passive, and malicious/active corruption.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
2 / 2
100% of queries surface data61/MP-SPDZ
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
13%
Of all named tools, what % are you?
Top rival
SCALE-MAMBA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SCALE-MAMBA · recommended 2×
  2. Obliv-C · recommended 2×
  3. FHEW · recommended 1×
  4. TFHE · recommended 1×
  5. ABY · recommended 1×
  • CATEGORY QUERY
    What are robust frameworks for secure multi-party computation to protect sensitive data privacy?
    you: #1
    AI recommended (in order):
    1. MP-SPDZ ← you
    2. FHEW
    3. TFHE
    4. ABY
    5. SCALE-MAMBA
    6. Obliv-C
    7. EzPC
    Show full AI answer
  • CATEGORY QUERY
    How to compare and benchmark different multi-party computation protocols and security models?
    you: #1
    AI recommended (in order):
    1. MP-SPDZ ← you
    2. SEAL
    3. HElib
    4. TFHE-rs
    5. Concrete-ML
    6. ABY3
    7. SCALE-MAMBA
    8. Obliv-C
    9. Sharemind
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of data61/MP-SPDZ?
    pass
    AI named data61/MP-SPDZ explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts data61/MP-SPDZ in production, what risks or prerequisites should they evaluate first?
    pass
    AI named data61/MP-SPDZ explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo data61/MP-SPDZ solve, and who is the primary audience?
    pass
    AI named data61/MP-SPDZ explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)
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data61/MP-SPDZ — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite