CPS security

This work pertains to the detection and prevention of attacks on cyber-physical systems, ranging from small home IoT networks, to networks of enterprise IoT devices, to electrical grid installations. We work on several topics in CPS security: One line of work is aimed at automatically deriving component configurations that optimally balance the security and functionality of a composed IoT system. A second line of work is aimed at developing secure and usable workflows involving mobile phones and IoT devices.

data privacy

This research is concerned with inferring knowledge from data in such a way that the data, or the algorithm, or the inference have to be protected from adversaries. The approaches followed depend upon the kinds of security and privacy guarantees desired and the strength of the adversary. We employ computationally secure primitives, such as homomorphic encryption, for manipulating data in the encrypted domain. We also investigate unconditionally secure approaches such as secret sharing, and statistically private mechanisms based on differential privacy. For differentially private methods, in particular, we are working on new approaches that preserve privacy in online, active learning scenarios.

Some representative publications:

1. S. Rane and P. Boufounos, Privacy-Preserving Nearest Neighbor Methods, IEEE Signal Processing Magazine, Vol. 30, No. 2, pp. 18–28, March 2013. (link)
2. S. Rane, J. Freudiger, A. Brito, E. Uzun, Privacy, Efficiency and Fault Tolerance in Aggregate Computations on Massive Star Networks, IEEE Workshop on Information Forensics and Security (WIFS 2015), Rome, Italy, November 2015. (paper)

biometrics

This work encompasses the theory, software implementation, and standardization of secure fingerprint-based biometric recognition systems. As a biometric is an irreplaceable attribute of a person, loss or theft of the biometric leaves her vulnerable to spoofing, identity theft and loss of sensitive data. To insulate a user from such vulnerabilities, we construct biometric recognition methods that do not require the system to store a person’s biometric (or biometric features) in the clear. This work is interdisciplinary. It involves the use of image processing and machine learning to identify and extract discriminative features from human biometrics. To prevent the extracted features from being accessed by adversaries while also allowing the comparisons of test biometrics against the enrolled biometric identities, we have employed information-theoretic primitives, such as fuzzy vaults, and cryptographic primitives, such as homomorphic encryption.

Some representative publications:

1. S. Rane, Y. Wang, S. C. Draper and P. Ishwar, Secure Biometrics: Concepts, Authentication Architectures, and Challenges, IEEE Signal Processing Magazine, Vol. 30, No. 5, pp. 51–64, September 2013. (link)
2. Y. Wang, S. Rane, S. C. Draper and P. Ishwar, A Theoretical Analysis of Authentication, Privacy and Reusability Across Secure Biometric Systems, IEEE Trans. Information Forensics and Security, Vol. 7, No. 6, pp. 1825–1840, December 2012. (link)

signal embeddings

This research was concerned with efficient machine learning operations on high-dimensional signals, such as images and videos. The philosophy is to map high-dimensional signals into a low-dimensional vector space such that some aspects of the signal geometry are preserved without necessarily targetting faithful recovery of the signals themselves. Most of the work that we have done so far has centered around distance-preserving embeddings based on the Johnson-Lindenstrauss Lemma. Particularly, to communicate the signal embeddings from a client to a database server, it is necessary to first quantize them. We have extended the Johnson-Lindenstrauss Lemma to quantized embeddings. Furthermore, by carefully designing the quantizer, some embeddings can be endowed with an interesting information-theoretic privacy property. This makes them useful in the design of privacy-aware nearest neighbor protocols.

Some representative publications:

1. M. Li, S. Rane and P. Boufounos, Quantized Embeddings of Scale-Invariant Image Features for Mobile Augmented Reality, IEEE International Workshop on Multimedia Signal Processing (MMSP 2012), Banff, Canada, September 2012. (paper)
2. P. Boufounos and S. Rane, Secure Binary Embeddings for Privacy-Preserving Nearest Neighbors, IEEE International Workshop on Information Forensics and Security (WIFS 2011), Iguazu Falls, Brazil, November 2011. (paper)