Constant Amplitude (CA), Zero Auto Correlation (ZAC) sequences (or CAZAC sequences, aka perfect sequences) have numerous applications. We generalize the CAZAC notion to what we term as CASAC by permitting small autocorrelations (SAC). We extend Björck’s classification result of two-valued CAZAC sequences by providing a complete classification of all almost 2-valued (i.e., two-valued except for the first position which uses a third value) CASAC sequences. While Björck’s original work dealt only with primes p, we extend his ideas to any abelian group of order$v\equiv 1\pmod {4}$, as opposed to restricting just to the prime fields GF(p). Björck sequences have better ambiguity function than Zadoff-Chu sequences, making them suitable for radar and communications applications in the presence of high Doppler shifts. In fact, the discrete narrow band ambiguity function has an optimal bound in case of Björck sequences (as opposed to Gauss sequences). A one-parameter infinite family of CASAC we construct would have applications in Multiple-Input Multiple-Output (MIMO) areas. Toward MIMO applications, we introduce a performance measure we term as cross merit factor to study cross correlation behavior, generalizing the well-known notion of Golay Merit Factor (GMF).

Image extraction attacks on machine learning models seek to recover semantically meaningful training imagery from a trained classifier model. Such attacks are concerning because training data include sensitive information. Research has shown that extracting training images is generally much harder than model inversion, which attempts to duplicate the functionality of the model. In this paper, we use the RBF SVM classifier to show that we can extract individual training images from models trained on thousands of images, which refutes the notion that these attacks can only extract an "average" of each class. Also, we correct common misperceptions about black-box image extraction attacks and developing a deep understanding of why some trained models are vulnerable to our attack while others are not. Our work is the first to show semantically meaningful images extracted from the RB F SVM classifier.

Reputation systems provide an important basis for judging whether to interact with others in a networked system. Designing such systems is especially interesting in decentralized environments such as mobile ad-hoc networks, as there is no trusted authority to manage reputation feedback information. Such systems also come with significant privacy concerns, further complicating the issue. Researchers have proposed privacy-preserving decentralized reputation systems (PDRS) which ensure individual reputation information is not leaked. Instead, aggregate information is exposed. Unfortunately, in existing PDRS, when a party leaves the network, all of the reputation information they possess about other parties in the network leaves too. This is a significant problem when applying such systems to the kind of dynamic networks we see in mobile computing. In this article, we introduce dynamic, privacy-preserving reputation systems (Dyn-PDRS) to solve the problem. We enumerate the features that a reputation system must support in order to be considered a Dyn-PDRS. Furthermore, we present protocols to enable these features and describe how our protocols are composed to form a Dyn-PDRS. We present simulations of our ideas to understand how a Dyn-PDRS impacts information availability in the network, and report on an implementation of our protocols, including timing experiments.

The smart grid offers an exciting way to better manage various aspects of a given utility. This comes with an increased threat of privacy due to fine-grained information reporting from each smart meter. Privacy preserving protocols have been proposed in the literature to deal with this problem. In this paper, we look at optimizing one technique, secure multiparty computation (MPC), for application to in-network, privacy preserving computation for smart meter networks. We propose a new construction for secure MPC, which we call transferable MPC. We present protocols for this construction, and show how this leads to more efficient and scalable multiparty computations in a smart metering application.