Computationally effective range migration compensation in PCL systems for maritime surveillance

In this paper, we consider the possibility of extending the coherent processing interval (CPI) as a way to improve target detection capability in passive radars for maritime surveillance applications. Despite the low velocity of the considered targets, range walk effects could limit the performance of the system when long CPIs are considered. To overcome these limitations while keeping the computational load controlled, we resort to a sub-optimal implementation of the Keystone Transform (KT), based on Lagrange polynomial interpolation, recently presented by the authors and successfully applied against aerial targets. Following those promising results, we extend the proposed approach to a coastal surveillance scenario. In the considered case, since longer CPI values are used, the proposed strategy appears to be even more attractive with respect to a conventional KT implementation based on the Chirp-Z Transform interpolation. In fact, comparable detection performance are obtained with a remarkable computational load saving. In detail, the effectiveness of the proposed approach is demonstrated against experimental data provided by Leonardo S.p.A., using a DVB-T based passive radar

Hard Communication Channels for Steganography

This paper considers steganography - the concept of hiding the presence of secret messages in legal communications - in the computational setting and its relation to cryptography. Very recently the first (non-polynomial time) steganographic protocol has been shown which, for any communication channel, is provably secure, reliable, and has nearly optimal bandwidth. The security is unconditional, i.e. it does not rely on any unproven complexity-theoretic assumption. This disproves the claim that the existence of one-way functions and access to a communication channel oracle are both necessary and sufficient conditions for the existence of secure steganography in the sense that secure and reliable steganography exists independently of the existence of one-way functions. In this paper, we prove that this equivalence also does not hold in the more realistic setting, where the stegosystem is polynomial time bounded. We prove this by constructing (a) a channel for which secure steganography exists if and only if one-way functions exist and (b) another channel such that secure steganography implies that no one-way functions exist. We therefore show that security-preserving reductions between cryptography and steganography need to be treated very carefully

On Pseudorandom Encodings

We initiate a study of pseudorandom encodings: efficiently computable and decodable encoding functions that map messages from a given distribution to a random-looking distribution. For instance, every distribution that can be perfectly and efficiently compressed admits such a pseudorandom encoding. Pseudorandom encodings are motivated by a variety of cryptographic applications, including password-authenticated key exchange, “honey encryption” and steganography. The main question we ask is whether every efficiently samplable distribution admits a pseudorandom encoding. Under different cryptographic assumptions, we obtain positive and negative answers for different flavors of pseudorandom encodings, and relate this question to problems in other areas of cryptography. In particular, by establishing a twoway relation between pseudorandom encoding schemes and efficient invertible sampling algorithms, we reveal a connection between adaptively secure multiparty computation for randomized functionalities and questions in the domain of steganography

A privacy-preserving fuzzy interest matching protocol for friends finding in social networks

Nowadays, it is very popular to make friends, share photographs, and exchange news throughout social networks. Social networks widely expand the area of people’s social connections and make communication much smoother than ever before. In a social network, there are many social groups established based on common interests among persons, such as learning group, family group, and reading group. People often describe their profiles when registering as a user in a social network. Then social networks can organize these users into groups of friends according to their profiles. However, an important issue must be considered, namely many users’ sensitive profiles could have been leaked out during this process. Therefore, it is reasonable to design a privacy-preserving friends-finding protocol in social network. Toward this goal, we design a fuzzy interest matching protocol based on private set intersection. Concretely, two candidate users can first organize their profiles into sets, then use Bloom filters to generate new data structures, and finally find the intersection sets to decide whether being friends or not in the social network. The protocol is shown to be secure in the malicious model and can be useful for practical purposes.Peer ReviewedPostprint (author's final draft