Biblio

Recently, a mechanism that randomly shuffles the data sent and allows securing the communication without the need to encrypt all the information has been proposed. This proposal is ideal for IoT systems with low computational capacity. In this work, we analyze the strength of this proposal from a brute-force attack approach to obtain the original message without knowledge of the applied disordering. It is demonstrated that for a set of 10x10 16-bit data, the processing time and the required memory are unfeasible with current technology. Therefore, it is safe.

Balancing utility and differential privacy by shuffling or BUDS is an approach towards crowd sourced, statistical databases, with strong privacy and utility balance using differential privacy theory. Here, a novel algorithm is proposed using one-hot encoding and iterative shuffling with the loss estimation and risk minimization techniques, to balance both the utility and privacy. In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report. During empirical test of balanced utility and privacy, BUDS produces ε = 0.02 which is a very promising result. Our algorithm maintains a privacy bound of ε = ln[t/((n1-1)S)] and loss bound of c'\textbackslashtextbareln[t/((n1-1)S)]-1\textbackslashtextbar.

Among the several threats to cyber services Distributed denial-of-service (DDoS) attack is most prevailing nowadays. DDoS involves making an online service unavailable by flooding the bandwidth or resources of a targeted system. It is easier for an insider having legitimate access to the system to circumvent any security controls thus resulting in insider attack. To mitigate insider assisted DDoS attacks, this paper proposes a moving target defense mechanism that involves isolation of insiders from innocent clients by using attack proxies. Further using the concept of load balancing an effective algorithm to detect and handle insider attack is developed with the aim of maximizing attack isolation while minimizing the total number of proxies used.

A fresh look at the way secure communications is currently being done has been undertaken as a consequence of the large hacking's that have taken place recently. A plausible option maybe a return to the future via Morse code using how a quantum bit (Qubit) reacts when entangled to suggest a cypher. This quantum cyphers uses multiple properties of unique entities that have many random radicals which makes hacking more difficult that traditional 'Rivest-Shamir-Adleman' (RSA), 'Digital Signature Algorithm' (DSA) or 'Elliptic Curve Digital Signature Algorithm' (ECDSA). Additional security is likely by Backchannelling (slipstreaming) Quantum Morse code (Q-Morse) keys composed of living and non-living entities. This means Blockchain ledger history (forwards-backwards) is audited during an active session. Verification keys are Backchannelling (slipstreaming) during the session (e.g. train driver must incrementally activate a switch otherwise the train stops) using predicted-expected sender-receiver properties as well as their past history of disconformities to random radicals encountered. In summary, Quantum Morse code (Q-Morse) plausibly is the enabler to additional security by Backchannelling (slipstreaming) during a communications session.

Polynomial masking is a glitch-resistant and higher-order masking scheme based upon Shamir's secret sharing scheme and multi-party computation protocols. Polynomial masking was first introduced at CHES 2011, while a 1st-order implementation of the AES S-box on FPGA was presented at CHES 2013. In this latter work, the authors showed a 2nd-order univariate leakage by side-channel collision analysis on a tuned measurement setup. This negative result motivates the need to evaluate the performance, area-costs, and security margins of combined \shuffled\ and higher-order polynomially masking schemes to counteract trivial univariate leakages. In this work, we provide the following contributions: first, we introduce additional principles for the selection of efficient addition chains, which allow for more compact and faster implementations of cryptographic S-boxes. Our 1st-order AES S-box implementation requires approximately 27% less registers, 20% less clock cycles, and 5% less random bits than the CHES 2013 implementation. Then, we propose a lightweight shuffling countermeasure, which inherently applies to polynomial masking schemes and effectively enhances their univariate security at negligible area expenses. Finally, we present the design of a \combined\ \shuffled\ \and\ higher-order polynomially masked AES S-box in hardware, while providing ASIC synthesis and side-channel analysis results in the Electro-Magnetic (EM) domain.

Polynomial masking is a glitch-resistant and higher-order masking scheme based upon Shamir's secret sharing scheme and multi-party computation protocols. Polynomial masking was first introduced at CHES 2011, while a 1st-order implementation of the AES S-box on FPGA was presented at CHES 2013. In this latter work, the authors showed a 2nd-order univariate leakage by side-channel collision analysis on a tuned measurement setup. This negative result motivates the need to evaluate the performance, area-costs, and security margins of combined \shuffled\ and higher-order polynomially masking schemes to counteract trivial univariate leakages. In this work, we provide the following contributions: first, we introduce additional principles for the selection of efficient addition chains, which allow for more compact and faster implementations of cryptographic S-boxes. Our 1st-order AES S-box implementation requires approximately 27% less registers, 20% less clock cycles, and 5% less random bits than the CHES 2013 implementation. Then, we propose a lightweight shuffling countermeasure, which inherently applies to polynomial masking schemes and effectively enhances their univariate security at negligible area expenses. Finally, we present the design of a \combined\ \shuffled\ \and\ higher-order polynomially masked AES S-box in hardware, while providing ASIC synthesis and side-channel analysis results in the Electro-Magnetic (EM) domain.

We introduce a cloud-enabled defense mechanism for Internet services against network and computational Distributed Denial-of-Service (DDoS) attacks. Our approach performs selective server replication and intelligent client re-assignment, turning victim servers into moving targets for attack isolation. We introduce a novel system architecture that leverages a "shuffling" mechanism to compute the optimal re-assignment strategy for clients on attacked servers, effectively separating benign clients from even sophisticated adversaries that persistently follow the moving targets. We introduce a family of algorithms to optimize the runtime client-to-server re-assignment plans and minimize the number of shuffles to achieve attack mitigation. The proposed shuffling-based moving target mechanism enables effective attack containment using fewer resources than attack dilution strategies using pure server expansion. Our simulations and proof-of-concept prototype using Amazon EC2 [1] demonstrate that we can successfully mitigate large-scale DDoS attacks in a small number of shuffles, each of which incurs a few seconds of user-perceived latency.