Secure Code Update for Embedded Devices via Proofs of Secure Erasure

Abstract. Remote attestation is the process of verifying internal state of a remote embedded device. It is an important component of many security protocols and applications. Although previously proposed re-mote attestation techniques assisted by specialized secure hardware are effective, they not yet viable for low-cost embedded devices. One no-table alternative is software-based attestation, that is both less costly and more efficient. However, recent results identified weaknesses in some proposed software-based methods, thus showing that security of remote software attestation remains a challenge. Inspired by these developments, this paper explores an approach that relies neither on secure hardware nor on tight timing constraints typi-cal of software-based technqiques. By taking advantage of the bounded memory/storage model of low-cost embedded devices and assuming a small amount of read-only memory (ROM), our approach involves a new primitive – Proofs of Secure Erasure (PoSE-s). We also show that, even though it is effective and provably secure, PoSE-based attestation is not cheap. However, it is particularly well-suited and practical for two other related tasks: secure code update and secure memory/storage erasure. We consider several flavors of PoSE-based protocols and demonstrate their feasibility in the context of existing commodity embedded devices.

Privacy Preserving Nearest Neighbor Search

Data mining is frequently obstructed by privacy concerns. In many cases, data is distributed and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this paper we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification. We prove the security of these algorithms under the semi-honest adversarial model, and describe methods that can be used to optimize their performance

Privacy Preserving Nearest Neighbor Search

Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this paper we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification. We prove the security of these algorithms under the semi-honest adversarial model, and describe methods that can be used to optimize their performance.

A Multi-Step Framework for Detecting Attack Scenarios

With growing dependence upon interconnected networks, defending these networks against intrusions is becoming increasingly important. In the case of attacks that are composed of multiple steps, detecting the entire attack scenario is of vital importance. In this paper, we propose an analysis framework that is able to detect these scenarios with little predefined information. The core of the system is the decomposition of the analysis into two steps: first detecting a few events in the attack with high confidence, and second, expanding from these events to determine the remainder of the events in the scenario. Our experiments show that we can accurately identify the majority of the steps contained within the attack scenario with relatively few false positives. Our framework can handle sophisticated attacks that are highly distributed, try to avoid standard pre-defined attack patterns, use cover traffic or “noisy ” attacks to distract analysts and draw attention away from the true attack, and attempt to avoid detection by signaturebased schemes through the use of novel exploits or mutation engines