Data Mining Practical Machine Learning Tools and Techniques

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

We study the problem of searching on data that is encrypted using a public key system. Consider user Bob who sends email to user Alice encrypted under Alice’s public key. An email gateway wants to test whether the email contains the keyword “urgent” so that it could route the email accordingly. Alice, on the other hand does not wish to give the gateway the ability to decrypt all her messages. We define and construct a mechanism that enables Alice to provide a key to the gateway that enables the gateway to test whether the word “urgent” is a keyword in the email without learning anything else about the email. We refer to this mechanism as Public Key Encryption with keyword Search. As another example, consider a mail server that stores various messages publicly encrypted for Alice by others. Using our mechanism Alice can send the mail server a key that will enable the server to identify all messages containing some specific keyword, but learn nothing else. We define the concept of public key encryption with keyword search and give several constructions.

https://link.springer.com/content/pdf/10.1007%2F978-3-540-24676-3_30.pdf

Public Key Encryption with Keyword Search

Series Preface * Preface to the Third Edition * 1 Linear Systems * 2 Nonlinear Systems: Local Theory * 3 Nonlinear Systems: Global Theory * 4 Nonlinear Systems: Bifurcation Theory * References * Index

/pdf/differential-equations-and-dynamical-systems-3chsjx2k45.pdf

Differential Equations and Dynamical Systems

Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

/pdf/the-knowledge-complexity-of-interactive-proof-systems-31apre0ecf.pdf

The knowledge complexity of interactive proof-systems

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with.

/pdf/vuldeepecker-a-deep-learning-based-system-for-vulnerability-3b0bpu5rbk.pdf

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Cryptographic computations (decryption, signature generation, etc.) are often performed on a relatively insecure device (e.g., a mobile device or an Internet-connected host) which cannot be trusted to maintain secrecy of the private key. We propose and investigate the notion of key-insulated security whose goal is to minimize the damage caused by secret-key exposures. In our model, the secret key(s) stored on the insecure device are refreshed at discrete time periods via interaction with a physically-secure - but computationally-limited - device which stores a "master key". All cryptographic computations are still done on the insecure device, and the public key remains unchanged. In a (t, N)-key-insulated scheme, an adversary who compromises the insecure device and obtains secret keys for up to t periods of his choice is unable to violate the security of the cryptosystem for any of the remaining N-t periods. Furthermore, the scheme remains secure (for all time periods) against an adversary who compromises only the physically-secure device. We focus primarily on key-insulated public-key encryption. We construct a (t, N)-key-insulated encryption scheme based on any (standard) publickey encryption scheme, and give a more efficient construction based on the DDH assumption. The latter construction is then extended to achieve chosen-ciphertext security.

/pdf/key-insulated-public-key-cryptosystems-48i2v4h92d.pdf

Key-Insulated Public Key Cryptosystems

A prerequisite for a secure communication between two nodes in an ad hoc network is that the nodes share a key to bootstrap their trust relationship. In this paper, we present a scalable and distributed protocol that enables two nodes to establish a pairwise shared key on the fly, without requiring the use of any on-line key distribution center. The design of our protocol is based on a novel combination of two techniques - probabilistic key sharing and threshold secret sharing. Our protocol is scalable since every node only needs to possess a small number of keys, independent of the network size, and it is computationally efficient because it only relies on symmetric key cryptography based operations. We show that a pairwise key established between two nodes using our protocol is secure against a collusion attack by up to a certain number of compromised nodes. We also show through a set of simulations that our protocol can be parameterized to meet the desired levels of performance, security and storage for the application under consideration.

https://sce.uhcl.edu/yang/teaching/csci5931WSNfall2007/WSN%20Papers/Zhu,%20Establishing%20Pairwise%20Keys%20for%20Secure%20Communication%20in%20Ad%20Hoc%20Networks%20A%20Probabilistic%20Approach.pdf

Establishing pairwise keys for secure communication in ad hoc networks: a probabilistic approach

/pdf/vabks-verifiable-attribute-based-keyword-search-over-39bq2o2g1b.pdf

VABKS: Verifiable attribute-based keyword search over outsourced encrypted data

/pdf/vuldeepecker-a-deep-learning-based-system-for-vulnerability-kapefh8bf9.pdf

Shouhuai Xu

Papers

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

Key-Insulated Public Key Cryptosystems

Establishing pairwise keys for secure communication in ad hoc networks: a probabilistic approach

VABKS: Verifiable attribute-based keyword search over outsourced encrypted data

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection