About

Carl Gunter is the George and Ann Fisher Distinguished Professor in Engineering at the University of Illinois Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science within The Grainger College of Engineering. His research areas include Security and Privacy, Systems and Networking. Gunter has taught courses such as Computer Security II, Topics in Societal Impacts, Cyber Dystopia, and Security and Privacy for IoT in Homes, focusing on the societal and technological aspects of security and privacy in computing systems. His work involves protecting genomic privacy through phone applications and advancing cybersecurity and critical infrastructure security through AI-focused projects. Gunter's contributions are recognized within the academic community, and he is actively involved in research that addresses societal impacts of computing, security, and privacy issues.

Research topics

• Computer Science
• Computer Security
• Data Mining
• Machine Learning
• Artificial Intelligence
• Human–computer interaction
• Engineering
• Internet privacy
• Geology
• Data science

Selected publications

• Help Me Help You: Privacy Considerations for Third Party IoT Device Repair

  Proceedings on Privacy Enhancing Technologies · 2025-07-13

  articleOpen access

  Smart home devices are becoming increasingly complex and data-rich. The inevitable repair of these devices will be both difficult and privacy-sensitive. A "HandyTech"—a technician for home Internet of Things (IoT) system repair—has the potential to lower barriers to repair, but privacy questions remain: Are people willing to use a HandyTech to fix a broken home IoT device despite the inherent privacy risk (i.e., allowing a third party to access potentially sensitive IoT data)? We explore this question through a vignette-based, multi-factorial survey with a nationally representative sample of adults in the United States. We further ask whether types of devices (i.e., smart speakers, refrigerators, and CPAP machines) and factors adjacent to privacy and associated with the HandyTech's work (i.e., scope of access, state-based licensing requirements, and transparency provisions) affect decisions to use or not use a HandyTech. We find that some demographic groups are more willing than others to use a HandyTech (e.g., younger age groups, those with children in the home). Current ownership of more types of smart devices increases willingness to use a HandyTech, while greater concerns over general IoT privacy decreases willingness to use a HandyTech. Device-specific perceptions also mattered, such that perceived urgency to fix is strongly associated with willingness to use a HandyTech, but concern over that device's privacy is not. In addition, reduced scope of access and increased transparency by the HandyTech statistically increased willingness to use a HandyTech. In closing, we recommend takeaways that developers and policymakers can engage with to decrease privacy concerns and increase the adoption of third-party IoT repair.

  PublisherDOI

• Termite Attacks: Gnawing on Logs to Extract Secret Information

  2025-05-05

  articleSenior author

  System audit logs aim to record information about system events that are triggered during any incident. Hence they inevitably contain sensitive information that needs to be kept secret. In particular, researchers have overlooked implicit secret content: content that is seemingly not secret but indirectly leaks secret information. We introduce Termite attacks, a new class of side channel attack that exploits such implicit content to learn secret information. Our work explores Termite attacks through three diverse cases showing how, under reasonable disclosure conditions, logs can be exploited by indirect inferences that produce significant side channels. We demonstrate how logs can be used to create both membership inference attacks and keystroke timing attacks, and show practical exploits against a common setup: a LEMP webserver with SSH daemon that is audited by the Linux Audit System. Then we demonstrate how to launch a concurrencybased timing side channel attack. Contrary to the belief that log information is too coarse-grained to be exploited against modern microarchitectural side channel attacks, we demonstrate how adversaries can craft timing side channels that are as fine as 100s of nanoseconds through system audit logs. Our proof-of-concept NetSpectre attack shows that adversary can steal a secret bit in 470 seconds with 81.25 % success rate with 12 memory loads.

  PublisherDOI

• We Need a “Building Inspector for IoT” When Smart Homes Are Sold

  IEEE Security & Privacy · 2024-05-07

  articleOpen access

  Internet of Things (IoT) devices left behind when a home is sold create security and privacy concerns for both prior and new residents. We envision a specialized “building inspector for IoT” to help securely facilitate transfer of the home.

  PublisherOA PDFDOI

• Designing and Evaluating a Testbed for the Matter Protocol: Insights into User Experience

  2024-01-01

  articleOpen access

  As the integration of smart devices into our daily environment accelerates, the vision of a fully integrated smart home is becoming more achievable through standards such as the Matter protocol.In response, this research paper explores the use of Matter in addressing the heterogeneity and interoperability problems of smart homes.We built a testbed and introduce a network utility device, designed to sniff network traffic and provide a wireless access point within IoT networks.This paper also presents the experience of students using the testbed in an academic scenario.

  PublisherOA PDFDOI

• A Tagging Solution to Discover IoT Devices in Apartments

  Annual Computer Security Applications Conference · 2023-12-02 · 1 citations

  article

  The number of Internet of Things (IoT) devices in smart homes is increasing. This broad adoption facilitates users’ lives, but it also brings problems. One such issue is that some IoT devices may invade users’ privacy through obscure data collection practices or hidden devices. Specific IoT devices can exist out of sight and still collect user data to send to third parties via the Internet. Owners can easily forget the location or even the existence of these devices, especially if the owner is a landlord managing several properties. The landlord-owner scenario creates multi-user problems as designers typically build IoT devices for single users. We developed tag models that use wireless protocols, buzzers, and LED lighting to guide users toward the hidden device in shared spaces and accommodate multi-user scenarios. They are attached to IoT devices inside a residential unit during their installation to be later discovered by a tenant. These tags are similar to Tile models or Airtag but have different features based on our privacy use case. For instance, our tags do not require pairing; multiple users can interact with them through our Android application. Our tags can also embed the IoT device’s information while protecting against unwanted access to that information through a proximity requirement. Researchers have developed several other tools, such as thermal cameras or virtual reality (VR), for discovering devices, but we focused on wireless technologies. We measured specific performance metrics of our tags to analyze their feasibility for this problem. We also conducted a user study to measure the participants’ comfort levels while finding objects with our tags attached. Our results indicate that wireless tags can be viable for device tracking in residential properties.

  PublisherDOI

• EDoG: Adversarial Edge Detection For Graph Neural Networks

  2023-02-01 · 11 citations

  article

  Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node (or subgraph) classification prediction by adding subtle perturbations. In particular, several attacks against GNNs have been proposed by adding/deleting a small amount of edges, which have caused serious security concerns. Detecting these attacks is challenging due to the small magnitude of perturbation and the discrete nature of graph data. In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation. Specifically, we propose a novel graph generation approach combined with link prediction to detect suspicious adversarial edges. To effectively train the graph generative model, we sample several sub-graphs from the given graph data. We show that since the number of adversarial edges is usually low in practice, with low probability the sampled sub-graphs will contain adversarial edges based on the union bound. In addition, considering the strong attacks which perturb a large number of edges, we propose a set of novel features to perform outlier detection as the preprocessing for our detection. Extensive experimental results on three real-world graph datasets including a private transaction rule dataset from a major company and two types of synthetic graphs with controlled properties (e.g., Erdos-Renyi and scale-free graphs) show that EDoG can achieve above 0.8 AUC against four state-of-the-art unseen attack strategies without requiring any knowledge about the attack type (e.g., degree of the target victim node); and around 0.85 with knowledge of the attack type. EDoG significantly outperforms traditional malicious edge detection baselines. We also show that an adaptive attack with full knowledge of our detection pipeline is difficult to bypass it. Our results shed light on several principles to improve the robustness of GNNs.

  PublisherDOI

• The HandyTech's Coming Between 1 and 4: Privacy Opportunities and Challenges for the IoT Handyperson

  2023-11-23 · 2 citations

  articleOpen access

  Smart homes are gaining popularity due to their convenience and efficiency, both of which come at the expense of increased complexity of Internet of Things (IoT) devices. Due to the number and heterogeneity of IoT devices, technologically inexperienced or time-burdened residents are unlikely to manage the setup and maintenance of IoT apps and devices. We highlight the need for a "HandyTech": a technically skilled contractor who can set up, repair, debug, monitor, and troubleshoot home IoT systems. In this paper, we consider the potential privacy challenges posed by the HandyTech, who has the ability to access IoT devices and private data. We do so in the context of single and multi-user smart homes, including rental units, condominiums, and temporary guests or workers. We examine the privacy harms that can arise when a HandyTech has legitimate access to information, but uses it in unintended ways. By providing insights for the development of privacy control policies and measures in-home IoT environments in the presence of the HandyTech, we capture the privacy concerns raised by other visitors to the home, including temporary residents, part-time workers, etc. This helps lay a foundation for the broad set of privacy concerns raised by home IoT systems.

  PublisherOA PDFDOI

• Familial Searches, The Fourth Amendment, and Genomic Control

  SSRN Electronic Journal · 2023-01-01

  articleOpen accessSenior author
  PublisherDOI

• Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing

  arXiv (Cornell University) · 2022

  • Computer Science
  • Computer Science
  • Data science

  In 2021, the Coordinated Science Laboratory CSL, an Interdisciplinary Research Unit at the University of Illinois Urbana-Champaign, hosted the Future of Computing Symposium to celebrate its 70th anniversary. CSL's research covers the full computing stack, computing's impact on society and the resulting need for social responsibility. In this white paper, we summarize the major technological points, insights, and directions that speakers brought forward during the Future of Computing Symposium. Participants discussed topics related to new computing paradigms, technologies, algorithms, behaviors, and research challenges to be expected in the future. The symposium focused on new computing paradigms that are going beyond traditional computing and the research needed to support their realization. These needs included stressing security and privacy, the end to end human cyber physical systems and with them the analysis of the end to end artificial intelligence needs. Furthermore, advances that enable immersive environments for users, the boundaries between humans and machines will blur and become seamless. Particular integration challenges were made clear in the final discussion on the integration of autonomous driving, robo taxis, pedestrians, and future cities. Innovative approaches were outlined to motivate the next generation of researchers to work on these challenges. The discussion brought out the importance of considering not just individual research areas, but innovations at the intersections between computing research efforts and relevant application domains, such as health care, transportation, energy systems, and manufacturing.

  PublisherOA PDFDOI

• A Tagging Solution to Discover IoT Devices in Apartments

  arXiv (Cornell University) · 2022-10-13

  preprintOpen accessSenior author

  The number of IoT devices in smart homes is increasing. This broad adoption facilitates users' lives, but it also brings problems. One such issue is that some IoT devices may invade users' privacy. Some reasons for this invasion can stem from obscure data collection practices or hidden devices. Specific IoT devices can exist out of sight and still collect user data to send to third parties via the Internet. Owners can easily forget the location or even the existence of these devices, especially if the owner is a landlord who manages several properties. The landlord-owner scenario creates multi-user problems as designers build machines for single users. We developed tags that use wireless protocols, buzzers, and LED lighting to lead users to solve the issue of device discovery in shared spaces and accommodate multi-user scenarios. They are attached to IoT devices inside a unit during their installation to be later discovered by a tenant. These tags have similar functionalities as the popular Tile models or Airtag, but our tags have different features based on our privacy use case. Our tags do not require pairing; multiple users can interact with them through our Android application. Although researchers developed several other tools, such as thermal cameras or virtual reality (VR), for discovering devices in environments, they have not used wireless protocols as a solution. We measured specific performance metrics of our tags to analyze their feasibility for this problem. We also conducted a user study to measure the participants' comfort levels while finding objects with our tags attached. Our results indicate that wireless tags can be viable for device tracking in residential properties.

  PublisherOA PDFDOI

Recent grants

• CT-ISG: Attribute-based Security and Messaging

  NSF · $362k · 2007–2010

• Collaborative Research: CT-T: DoS Prevention in Shared Channels

  NSF · $530k · 2005–2009

• TWC: Small: Friendsourcing to Detect Network Manipulation

  NSF · $500k · 2012–2017

• TC: Medium: Collaborative Research: Experience-Based Access Management (EBAM) for Hospital Information Technology

  NSF · $400k · 2010–2016

• TWC: Frontier: Collaborative: Enabling Trustworthy Cybersystems for Health and Wellness

  NSF · $2.0M · 2013–2020

Frequent coauthors

• Bradley Malin

  University of Illinois Urbana-Champaign

  17 shared

• Gary McGraw

  Berry College

  16 shared

• Ludwig Fuchs

  16 shared

• Stefan Katzenbeisser

  University of Passau

  16 shared

• Strict Dista

  University of Illinois Urbana-Champaign

  16 shared

• Haya Shulman

  University of Insubria

  16 shared

• Elena Ferrari

  University of Insubria

  16 shared

• Günther Pernul

  University of Regensburg

  16 shared

Labs

• Siebel School of Computing and Data SciencePI

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1990

• M.S., Computer Science

  University of California, Berkeley

  1986

• B.S., Computer Science

  University of California, Los Angeles

  1983

Awards & honors

• George and Ann Fisher Distinguished Professor in Engineering