Digital Watermarking for Data Leakage Tracing: Techniques, Applications, and Challenges

With China’s digital economy reaching $5.4 trillion and projected to become the world’s largest data circle by 2025, data leakage has become a critical security concern, especially as 2020 saw more leaked records than the previous 15 years combined.

The talk outlines four main topics: the current state of data leaks, digital watermark technology, its use in e‑commerce, and open research questions.

Data‑leakage landscape – Leaks stem from system failures, human error, and malicious attacks, forming a black‑market chain that includes data harvesters, intermediaries, and buyers, with an estimated market value in the hundreds of billions.

Digital watermark fundamentals – Watermarks are imperceptible signals embedded in host data for provenance and copyright protection. The generic framework consists of a watermark‑embedding stage (encrypting the watermark with a key and inserting it) and an extraction stage (recovering the watermark to identify the source).

Evaluation metrics include invisibility, capacity, robustness, practicality, and security.

Watermark types – Image watermarks (LSB, DCT/DWT), text watermarks (spacing, zero‑width characters, natural‑language substitution), and database watermarks (reversible schemes for numeric and character fields). Each type has specific strengths and weaknesses, especially against AI‑driven removal attacks.

E‑commerce use cases – Sensitive user data (personal info, transaction details) is often leaked via screenshots, batch exports, or printed documents. Proposed protections involve front‑end visible watermarks combined with invisible (dark) watermarks for traceability, text watermarks for critical fields, and robust database watermarking to survive data processing.

Practical challenges – Affine transformations, compression, cropping, and AI‑based removal can degrade watermarks; database watermarks must resist sorting, filtering, and format changes. Solutions include multi‑layer watermarking, redundancy, error‑correcting codes, and selective embedding in high‑value attributes.

Future research directions – Developing universal, hard‑to‑remove watermarks; watermarking ultra‑short texts (e.g., phone numbers, ID numbers); optimizing computational and storage overhead; and handling merged datasets with multiple watermarks.

The session concludes with a Q&A discussing dark web‑page watermarks and the feasibility of extracting multiple watermarks from fused texts.