Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications

Computer Science > Networking and Internet Architecture

arXiv:2606.31973v1 (cs) [Submitted on 30 Jun 2026]

Title:Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications

Authors:Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus View a PDF of the paper titled Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications, by Yalin E. Sagduyu and 3 other authors View PDF HTML (experimental)

Abstract:Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robust communication under resource and channel constraints. In this paper, the privacy implications of relay-assisted SemCom systems are studied, where the intermediate relay node operates directly on learned latent representations. It is shown that the relay, even without access to source data, can reliably infer semantic meaning and reconstruct signals with performance comparable to that of the legitimate receiver, revealing a fundamental privacy vulnerability of semantic representations. To address this issue, an iterative adversarial training framework is proposed in which a strong, adaptively trained eavesdropper at the relay is explicitly accounted for. The proposed approach alternates between optimizing the relay's eavesdropping function and the legitimate system, resulting in representations that preserve semantic decoding performance at the intended receiver while degrading semantic inference at the relay. The semantic accuracy gap between the legitimate receiver and the eavesdropper is significantly enlarged across channel conditions. Importantly, this protection is achieved in a stealthy manner, with high reconstruction fidelity maintained while semantic leakage is selectively suppressed.

Subjects:	Networking and Internet Architecture (cs.NI); Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as:	arXiv:2606.31973 [cs.NI]
	(or arXiv:2606.31973v1 [cs.NI] for this version)
	https://doi.org/10.48550/arXiv.2606.31973 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tugba Erpek [view email]
[v1] Tue, 30 Jun 2026 17:15:23 UTC (314 KB)
Full-text links:

Access Paper:

View a PDF of the paper titled Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications, by Yalin E. Sagduyu and 3 other authors
• View PDF
• HTML (experimental)
• TeX Source

view license

Current browse context:

cs.NI < prev   |   next >
new | recent | 2026-06 Change to browse by: cs
cs.CR
cs.IT
cs.LG
eess
eess.SP
math
math.IT

References & Citations

• NASA ADS
• Google Scholar
• Semantic Scholar

export BibTeX citation Loading...

BibTeX formatted citation

× Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos

Demos

Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers

Recommenders and Search Tools

Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?)

• Author
• Venue
• Institution
• Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)