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- posterJune 2024
IoTFuzzSentry: Hunting Bugs In The IoT Wilderness In Operational Phase Using Payload Fuzzing
CODASPY '24: Proceedings of the Fourteenth ACM Conference on Data and Application Security and PrivacyJune 2024, pp 171–173https://doi.org/10.1145/3626232.3658642In the operational phase, an IoT device runs a light-weight server that is responsible for responding to the user queries, like accessing video and taking a snap in a IoT camera. The flaws in the implementation of certain security mechanisms in these IoT ...
- short-paperJune 2024
Privkit: A Toolkit of Privacy-Preserving Mechanisms for Heterogeneous Data Types
CODASPY '24: Proceedings of the Fourteenth ACM Conference on Data and Application Security and PrivacyJune 2024, pp 319–324https://doi.org/10.1145/3626232.3653284With the massive data collection from different devices, spanning from mobile devices to all sorts of IoT devices, protecting the privacy of users is a fundamental concern. In order to prevent unwanted disclosures, several Privacy-Preserving Mechanisms (...
- research-articleJune 2024
SLIM-View: Sampling and Private Publishing of Multidimensional Databases
CODASPY '24: Proceedings of the Fourteenth ACM Conference on Data and Application Security and PrivacyJune 2024, pp 391–402https://doi.org/10.1145/3626232.3653275Despite the enormous data processing capacity available in big data frameworks, obtaining appropriate and private responses to large-scale queries without revealing sensitive information is still a challenging problem. In this paper, we address the ...
- research-articleJune 2024
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning
CODASPY '24: Proceedings of the Fourteenth ACM Conference on Data and Application Security and PrivacyJune 2024, pp 199–210https://doi.org/10.1145/3626232.3653257Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is challenging. In particular, due to the sensitivity of the data each party might ...
- research-articleJune 2024
Anonymizing Test Data in Android: Does It Hurt?
AST '24: Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024)April 2024, pp 88–98https://doi.org/10.1145/3644032.3644463Failure data collected from the field (e.g., failure traces, bug reports, and memory dumps) represent an invaluable source of information for developers who need to reproduce and analyze failures. Unfortunately, field data may include sensitive ...
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- research-articleJune 2024
Alexa, is the skill always safe? Uncover Lenient Skill Vetting Process and Protect User Privacy at Run Time
ICSE-SEIS'24: Proceedings of the 46th International Conference on Software Engineering: Software Engineering in SocietyApril 2024, pp 34–45https://doi.org/10.1145/3639475.3640102Voice personal assistant (VPA) platforms (e.g., Amazon Alexa) allow developers to deploy their voice apps on third-party servers. However, this strategy introduces unexpected privacy risks to VPA customers. Malicious developers can dynamically change ...
- research-articleJune 2024
The unfair side of Privacy Enhancing Technologies: addressing the trade-offs between PETs and fairness
FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and TransparencyJune 2024, pp 2047–2059https://doi.org/10.1145/3630106.3659024Data sharing in the European Union (EU) has gained new momentum, among others for machine learning (ML) and artificial intelligence (AI) training purposes. By enabling models’ training whilst preserving the privacy of data, Privacy Enhancing Technologies ...
- demonstrationJune 2024
Demo : Privacy-Preserving Decentralized Machine Learning Framework for Clustered Resource-Constrained Devices
MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and ServicesJune 2024, pp 612–613https://doi.org/10.1145/3643832.3661843We present a secure decentralized learning framework suitable for resource-constrained devices within a cluster environment. Our approach focuses on enhancing privacy preservation during model aggregation by utilizing Differential Privacy. This technique ...
- short-paperJune 2024
Poster: Privacy in Distributed Mobile Networks
MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and ServicesJune 2024, pp 720–721https://doi.org/10.1145/3643832.3661438In order to achieve zero-knowledge proof (ZKP) in distributed mobile scenarios, we propose a two-stage multi-prover ZKP framework. Our method utilizes secure multi-party computation (MPC), which has advantages such as flexible adaptation, stable ...
- research-articleMay 2024
PrivacyCAT: Privacy-Aware Code Analysis at Scale
- Ke Mao,
- Cons Åhs,
- Sopot Cela,
- Dino Distefano,
- Nick Gardner,
- Radu Grigore,
- Per Gustafsson,
- Ákos Hajdu,
- Timotej Kapus,
- Matteo Marescotti,
- Gabriela Cunha Sampaio,
- Thibault Suzanne
ICSE-SEIP '24: Proceedings of the 46th International Conference on Software Engineering: Software Engineering in PracticeApril 2024, pp 106–117https://doi.org/10.1145/3639477.3639742Static and dynamic code analyses have been widely adopted in industry to enhance software reliability, security, and performance by automatically detecting bugs in the code. In this paper, we introduce PrivacyCAT1, a code analysis system developed and ...
- research-articleMay 2024
Privacy-Preserving Control of Partitioned Energy Resources
e-Energy '24: Proceedings of the 15th ACM International Conference on Future and Sustainable Energy SystemsJune 2024, pp 610–624https://doi.org/10.1145/3632775.3661988Distributed energy resources are an increasingly important part of the electric grid. We examine the problem of partitioning a distributed energy resource among many users while providing privacy to them. In this model, clients can send requests to a ...
- research-articleMay 2024
Counterfactual Explanation at Will, with Zero Privacy Leakage
Proceedings of the ACM on Management of Data (PACMMOD), Volume 2, Issue 3Article No.: 130, pp 1–29https://doi.org/10.1145/3654933While counterfactuals have been extensively studied as an intuitive explanation of model predictions, they still have limited adoption in practice due to two obstacles: (a) They rely on excessive access to the model for explanation that the model owner ...
- research-articleMay 2024
PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation
Proceedings of the ACM on Human-Computer Interaction (PACMHCI), Volume 8, Issue ETRAArticle No.: 232, pp 1–23https://doi.org/10.1145/3655606Latest gaze estimation methods require large-scale training data but their collection and exchange pose significant privacy risks. We propose PrivatEyes - the first privacy-enhancing training approach for appearance-based gaze estimation based on ...
- short-paperMay 2024
Over-the-Air Runtime Wi-Fi MAC Address Re-randomization
WiSec '24: Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile NetworksMay 2024, pp 8–13https://doi.org/10.1145/3643833.3656122Medium Access Control (MAC) address randomization is a key component for privacy protection in Wi-Fi networks. Current proposals periodically change the mobile device MAC addresses when it disconnects from the Access Point (AP). This way frames cannot be ...
- short-paperMay 2024
Privacy-Preserving Pseudonyms for LoRaWAN
WiSec '24: Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile NetworksMay 2024, pp 14–19https://doi.org/10.1145/3643833.3656120LoRaWAN, a widely deployed LPWAN protocol, raises privacy concerns due to metadata exposure, particularly concerning the exploitation of stable device identifiers. For the first time in literature, we propose two privacy-preserving pseudonym schemes ...
- research-articleMay 2024
DynamiQS: Quantum Secure Authentication for Dynamic Charging of Electric Vehicles
WiSec '24: Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile NetworksMay 2024, pp 174–184https://doi.org/10.1145/3643833.3656115Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows charging an electric vehicle while driving thanks to a dedicated road infrastructure. DWPT's capabilities in automatically establishing charging sessions and billing without users' ...
- short-paperMay 2024
Towards Safe, Secure, and Usable LLMs4Code
ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion ProceedingsApril 2024, pp 258–260https://doi.org/10.1145/3639478.3639803Large Language Models (LLMs) are gaining popularity in the field of Natural Language Processing (NLP) due to their remarkable accuracy in various NLP tasks. LLMs designed for coding are trained on massive datasets, which enables them to learn the ...
- posterMay 2024
SADHE: Secure Anomaly Detection for GPS Trajectory Based on Homomorphic Encryption
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied ComputingApril 2024, pp 139–141https://doi.org/10.1145/3605098.3636165Location-based services (LBS) have become a necessity in today's scenario. We enjoy these services through various applications such as Google Maps, Uber, Ola, Zomato, Swiggy, etc. These services facilitate many things, but at the same time, they ...
- research-articleMay 2024
A Game-theoretic Framework for Privacy-preserving Federated Learning
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 15, Issue 3Article No.: 52, pp 1–35https://doi.org/10.1145/3656049In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of privacy leakage cannot be ignored in the presence of semi-honest adversaries. Existing research has focused either on designing protection ...
- research-articleMay 2024
Ensuring Fairness and Gradient Privacy in Personalized Heterogeneous Federated Learning
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 15, Issue 3Article No.: 56, pp 1–30https://doi.org/10.1145/3652613With the increasing tension between conflicting requirements of the availability of large amounts of data for effective machine learning-based analysis, and for ensuring their privacy, the paradigm of federated learning has emerged, a distributed machine ...