lire-demain.fr
Aug 10, 2026
Data breach of Lire Demain (lire-demain.fr), a French educational book distribution company serving schools and municipalities primarily in the Seine-et-Marne (77) department. The breach contains client records (schools, colleges, municipalities with contact details and email addresses), order history with financial data, shipping/expedition records with personal names, addresses and phone numbers of individuals, invoices, and product catalog data. Data spans from at least 2020 through early 2026.
Data found in this dataset
Source files
Expand any file to inspect its column headers and the LLM's field-mapping reasoning, recorded during ingestion.
lire-demain.fr__clients__clients.csv4,140 rows
File structure
Notes: Pre-LLM auto-detection: free-form text with visible emails / phones
lire-demain.fr__commandes__suivie-de-commandes__commandes.csv13 columns1,944 rows
File structure
Format: CSV·Delimiter: Comma·Has header: yes·Quote: "
| Source column | Mapped field | Confidence | LLM assessment |
|---|---|---|---|
| 2 | fullName | high | [2] header 'nom_commercial', values are full names like 'Isabelle DILLIES' |
| 12 | fullName | high | [12] header 'cli_nom', values contain full names like 'LORINET Jessica', 'Brazier Priscilla' |
| 13 | phone | high | [13] header 'cli_telephone', values are French phone numbers with spaces and leading zeros |
| 14 | zip | high | [14] header 'cli_cp', values are French postal codes with '.0' suffix (e.g., '77410.0') |
| 15 | city | high | [15] header 'cli_ville', values are French city names like 'Chelles', 'NOISIEL' |
| 17 | fullName | high | [17] header 'cliliv_nom', values contain full names like 'LORINET Jessica', 'Brazier Priscilla' |
| 18 | phone | high | [18] header 'cliliv_telephone', values are French phone numbers without spaces (e.g., 615775838) |
| 19 | zip | high | [19] header 'cliliv_cp', values are French postal codes with '.0' suffix (e.g., '77500.0') |
| 20 | city | high | [20] header 'cliliv_ville', values are French city names like 'Chelles', 'NOISIEL' |
| 22 | fullName | high | [22] header 'clifact_nom', values contain full names like 'LORINET Jessica', 'Brazier Priscilla' |
| 23 | phone | high | [23] header 'clifact_telephone', values are French phone numbers without spaces (e.g., 615775838) |
| 24 | zip | high | [24] header 'clifact_cp', values are French postal codes with '.0' suffix (e.g., '77500.0') |
| 25 | city | high | [25] header 'clifact_ville', values are French city names like 'Chelles', 'NOISIEL' |
Notes: 44 total columns, 12 contain PII. Columns 0-1, 3-11, 16, 26-43 are non-PII (internal IDs, commercial codes, dates, amounts, statuses, counts, flags).
lire-demain.fr__expeditions__lirelete.csv3 columns475 rows
File structure
Format: CSV·Delimiter: Semicolon·Has header: yes·Quote: "
| Source column | Mapped field | Confidence | LLM assessment |
|---|---|---|---|
| 0 | lastName | high | header "nom" resolves to PII field "lastName" |
| 1 | firstName | high | header "prenom" resolves to PII field "firstName" |
| 8 | phone | high | header "telephone" resolves to PII field "phone" |
Notes: Heuristic auto-detection: header-named PII columns confirmed by data conformance
lire-demain.fr__facturations__factures.csv1 column2,238 rows
File structure
Format: CSV·Delimiter: Comma·Has header: yes·Quote: "
| Source column | Mapped field | Confidence | LLM assessment |
|---|---|---|---|
| 5 | fullName | high | [5] header 'Client', values contain full names like 'LORINET Jessica' and 'Brazier Priscilla' |
Notes: 25 columns total, only column 5 contains PII (full names). All other columns are transactional/invoice data (dates, amounts, statuses, codes) or internal IDs that do not contain PII. No emails, addresses, phones, or other personal identifiers are present in the sample.
lire-demain.fr__produits__produits.csv0 rows
File structure
Format: CSV·Delimiter: Semicolon·Has header: yes·Quote: "
Notes: The file contains only product catalog data (product codes, descriptions, prices) with no personal information. All columns represent product attributes, inventory status, and pricing — none map to PII fields. No emails, names, addresses, or other personal data appear in the sample.