More Like this

1. Physical Unclonable Function (PUF)-Based e-Cash Transaction Protocol (PUF-Cash)

   https://doi.org/10.3390/cryptography3030018  

   Calhoun, Jeff; Minwalla, Cyrus; Helmich, Charles; Saqib, Fareena; Che, Wenjie; Plusquellic, Jim (September 2019, Cryptography)

   null (Ed.)

   Electronic money (e-money or e-Cash) is the digital representation of physical banknotes augmented by added use cases of online and remote payments. This paper presents a novel, anonymous e-money transaction protocol, built based on physical unclonable functions (PUFs), titled PUF-Cash. PUF-Cash preserves user anonymity while enabling both offline and online transaction capability. The PUF’s privacy-preserving property is leveraged to create blinded tokens for transaction anonymity while its hardware-based challenge–response pair authentication scheme provides a secure solution that is impervious to typical protocol attacks. The scheme is inspired from Chaum’s Digicash work in the 1980s and subsequent improvements. Unlike Chaum’s scheme, which relies on Rivest, Shamir and Adlemans’s (RSA’s) multiplicative homomorphic property to provide anonymity, the anonymity scheme proposed in this paper leverages the random and unique statistical properties of synthesized integrated circuits. PUF-Cash is implemented and demonstrated using a set of Xilinx Zynq Field Programmable Gate Arrays (FPGAs). Experimental results suggest that the hardware footprint of the solution is small, and the transaction rate is suitable for large-scale applications. An in-depth security analysis suggests that the solution possesses excellent statistical qualities in the generated authentication and encryption keys, and it is robust against a variety of attack vectors including model-building, impersonation, and side-channel variants. 

   more » « less
2. Physical Unclonable Function (PUF)-based e-Cash Transaction Protocol (PUF-Cash)

   Jeff Calhoun, Cyrus Minwalla (January 2019, Cryptography)

   Electronic money (e‐money or e‐Cash) is the digital representation of physical banknotes augmented by added use cases of online and remote payments. This paper presents a novel, anonymous e‐money transaction protocol, built based on physical unclonable functions (PUFs), titled PUF‐Cash. PUF‐Cash preserves user anonymity while enabling both offline and online transaction capability. The PUF’s privacy‐preserving property is leveraged to create blinded tokens for transaction anonymity while its hardware‐based challenge–response pair authentication scheme provides a secure solution that is impervious to typical protocol attacks. The scheme is inspired from Chaum’s Digicash work in the 1980s and subsequent improvements. Unlike Chaum’s scheme, which relies on Rivest, Shamir and Adlemans’s (RSA’s) multiplicative homomorphic property to provide anonymity, the anonymity scheme proposed in this paper leverages the random and unique statistical properties of synthesized integrated circuits. PUF‐Cash is implemented and demonstrated using a set of Xilinx Zynq Field Programmable Gate Arrays (FPGAs). Experimental results suggest that the hardware footprint of the solution is small, and the transaction rate is suitable for large‐scale applications. An in‐depth security analysis suggests that the solution possesses excellent statistical qualities in the generated authentication and encryption keys, and it is robust against a variety of attack vectors including model‐building, impersonation, and side‐ channel variants. 

   more » « less
3. Continuous authentication of smartphone users by fusing typing, swiping, and phone movement patterns

   https://doi.org/10.1109/BTAS.2016.7791164  

   Kumar, Rajesh; Phoha, Vir V.; Serwadda, Abdul (September 2016, Biometrics Theory, Applications and Systems (BTAS), 2016 IEEE 8th International Conference on)

   We studied the fusion of three biometric authentication modalities, namely, swiping gestures, typing patterns and the phone movement patterns observed during typing or swiping. A web browser was customized to collect the data generated from the aforementioned modalities over four to seven days in an unconstrained environment. Several features were extracted by using sliding window mechanism for each modality and analyzed by using information gain, correlation, and symmetric uncertainty. Finally, five features from windows of continuous swipes, thirty features from windows of continuously typed letters, and nine features from corresponding phone movement patterns while swiping/typing were used to build the authentication system. We evaluated the performance of each modality and their fusion over a dataset of 28 users. The feature-level fusion of swiping and the corresponding phone movement patterns achieved an authentication accuracy of 93.33%, whereas, the score-level fusion of typing behaviors and the corresponding phone movement patterns achieved an authentication accuracy of 89.31 %. 

   more » « less
4. Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

   https://doi.org/10.1038/s41612-022-00293-z  

   Iyer, Shiva R.; Balashankar, Ananth; Aeberhard, William H.; Bhattacharyya, Sujoy; Rusconi, Giuditta; Jose, Lejo; Soans, Nita; Sudarshan, Anant; Pande, Rohini; Subramanian, Lakshminarayanan (October 2022, npj Climate and Atmospheric Science)

   Abstract The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments’ ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities. 

   more » « less
5. Multi-Modality Mobile Datasets for Behavioral Biometrics Research: Data/Toolset paper

   https://doi.org/10.1145/3577923.3583637  

   Ray-Dowling, Aratrika; Wahab, Ahmed Anu; Hou, Daqing; Schuckers, Stephanie (April 2023, CODASPY '23: Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy)

   The ubiquity of mobile devices nowadays necessitates securing the apps and user information stored therein. However, existing one-time entry-point authentication mechanisms and enhanced security mechanisms such as Multi-Factor Authentication (MFA) are prone to a wide vector of attacks. Furthermore, MFA also introduces friction to the user experience. Therefore, what is needed is continuous authentication that once passing the entry-point authentication, will protect the mobile devices on a continuous basis by confirming the legitimate owner of the device and locking out detected impostor activities. Hence, more research is needed on the dynamic methods of mobile security such as behavioral biometrics-based continuous authentication, which is cost-effective and passive as the data utilized to authenticate users are logged from the phone's sensors. However, currently, there are not many mobile authentication datasets to perform benchmarking research. In this work, we share two novel mobile datasets (Clarkson University (CU) Mobile datasets I and II) consisting of multi-modality behavioral biometrics data from 49 and 39 users respectively (88 users in total). Each of our datasets consists of modalities such as swipes, keystrokes, acceleration, gyroscope, and pattern-tracing strokes. These modalities are collected when users are filling out a registration form in sitting both as genuine and impostor users. To exhibit the usefulness of the datasets, we have performed initial experiments on selected individual modalities from the datasets as well as the fusion of simultaneously available modalities. 

   more » « less