Evidence map›Paper›PMID 42668764›Full record

ArticleICCAD. IEEE/ACM International Conference on Computer-Aided Design2025

Invited Paper: Security Under the Lens: Vulnerabilities in In-Sensor Computing Systems.

Mashrafi Kajol, Wei Lu, Qiaoyan Yu

Abstract read
In one paragraph

Article in ICCAD. IEEE/ACM International Conference on Computer-Aided Design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mashrafi KajolUniversity of New Hampshire, Durham, NH 03824, USA.
Wei LuKeene State College, Keene, NH 03435, USA.
Qiaoyan YuUniversity of New Hampshire, Durham, NH 03824, USA.

Funding

An Explainable Artificial Intelligence Based Hybrid Intrusion Detection System for Enhancing Healthcare SecurityR15EB036778 · NIBIB · KEENE STATE COLLEGE · PI LU, WEI · 2024 to 2024
$357k
NIBIB NIH HHS R15 EB036778
6 · The paper itself

Abstract

In-Sensor Computing (ISC) systems integrate sensing and computing units within a single device, enabling low-latency, energy-efficient applications through direct analog-to-feature conversion. However, the intrinsic tight coupling between sensing and computational components introduces significant security vulnerabilities. These arise particularly in scenarios where adversaries have a good understanding of the analog computation mechanisms and could tamper with the ISC device, potentially allowing for manipulation, inference, or extraction of sensitive data. This work introduces exploitable backdoors in ISC that encode the output of the analog computation unit to create covert channels. Through theoretical modeling and empirical case studies, we investigate two ISC-specific covert channels: a logic covert channel and a frequency covert channel. These channels are established by deliberately manipulating the analog computation unit co-integrated with sensing materials on a shared substrate, thereby enabling adversaries to exfiltrate sensitive information, posing substantial threats to the security and privacy of real-world ISC applications.

Indexed as

covert channel attackhardware securityIn-sensor computingmachine learningprivacy

Identifiers

PMID42668764
PMCPMC13525468

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.