SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search

The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential. SANNS comprises two protocols: an optimized linear scan and a protocol based on a novel sublinear time clustering-based algorithm. We prove the security of both protocols in the standard semi-honest model. The protocols are built upon several state-of-the-art cryptographic primitives such as lattice-based additively homomorphic encryption, distributed oblivious RAM, and garbled circuits. We provide several contributions to each of these primitives which are applicable to other secure computation tasks. Both of our protocols rely on a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from $O(n + k^2)$ comparators. We have implemented our proposed system and performed extensive experimental results on four datasets in two different computation environments, demonstrating more than $18-31\times$ faster response time compared to optimally implemented protocols from the prior work. Moreover, SANNS is the first work that scales to the database of 10 million entries, pushing the limit by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202

Privacy preserving encrypted phonetic search of speech data

This paper presents a strategy for enabling speech recognition to be performed in the cloud whilst preserving the privacy of users. The approach advocates a demarcation of responsibilities between the client and server-side components for performing the speech recognition task. On the client-side resides the acoustic model, which symbolically encodes the audio and encrypts the data before uploading to the server. The server-side then employs searchable encryption to enable the phonetic search of the speech content. Some preliminary results for speech encoding and searchable encryption are presented

Intelligent XML Tag Classification Techniques for XML Encryption Improvement

Flexibility, friendliness, and adaptability have been key components to use XML to exchange information across different networks providing the needed common syntax for various messaging systems. However excess usage of XML as a communication medium shed the light on security standards used to protect exchanged messages achieving data confidentiality and privacy. This research presents a novel approach to secure XML messages being used in various systems with efficiency providing high security measures and high performance. system model is based on two major modules, the first to classify XML messages and define which parts of the messages to be secured assigning an importance level for each tag presented in XML message and then using XML encryption standard proposed earlier by W3C [3] to perform a partial encryption on selected parts defined in classification stage. As a result, study aims to improve both the performance of XML encryption process and bulk message handling to achieve data cleansing efficiently

Stealthy Plaintext

Correspondence through email has become a very significant way of communication at workplaces. Information of most kinds such as text, video and audio can be shared through email, the most common being text. With confidential data being easily sharable through this method most companies monitor the emails, thus invading the privacy of employees. To avoid secret information from being disclosed it can be encrypted. Encryption hides the data effectively but this makes the data look important and hence prone to attacks to decrypt the information. It also makes it obvious that there is secret information being transferred. The most effective way would be to make the information seem harmless by concealing the information in the email but not encrypting it. We would like the information to pass through the analyzer without being detected. This project aims to achieve this by “encrypting” plain text by replacing suspicious keywords with non-suspicious English words, trying to keep the grammatical syntax of the sentences intact

Roadmap on optical security

Postprint (author's final draft

Applications of Artificial Intelligence to Cryptography

This paper considers some recent advances in the field of Cryptography using Artificial Intelligence (AI). It specifically considers the applications of Machine Learning (ML) and Evolutionary Computing (EC) to analyze and encrypt data. A short overview is given on Artificial Neural Networks (ANNs) and the principles of Deep Learning using Deep ANNs. In this context, the paper considers: (i) the implementation of EC and ANNs for generating unique and unclonable ciphers; (ii) ML strategies for detecting the genuine randomness (or otherwise) of finite binary strings for applications in Cryptanalysis. The aim of the paper is to provide an overview on how AI can be applied for encrypting data and undertaking cryptanalysis of such data and other data types in order to assess the cryptographic strength of an encryption algorithm, e.g. to detect patterns of intercepted data streams that are signatures of encrypted data. This includes some of the authors’ prior contributions to the field which is referenced throughout. Applications are presented which include the authentication of high-value documents such as bank notes with a smartphone. This involves using the antenna of a smartphone to read (in the near field) a flexible radio frequency tag that couples to an integrated circuit with a non-programmable coprocessor. The coprocessor retains ultra-strong encrypted information generated using EC that can be decrypted on-line, thereby validating the authenticity of the document through the Internet of Things with a smartphone. The application of optical authentication methods using a smartphone and optical ciphers is also briefly explored

Application of Stochastic Diffusion for Hiding High Fidelity Encrypted Images

Cryptography coupled with information hiding has received increased attention in recent years and has become a major research theme because of the importance of protecting encrypted information in any Electronic Data Interchange system in a way that is both discrete and covert. One of the essential limitations in any cryptography system is that the encrypted data provides an indication on its importance which arouses suspicion and makes it vulnerable to attack. Information hiding of Steganography provides a potential solution to this issue by making the data imperceptible, the security of the hidden information being a threat only if its existence is detected through Steganalysis. This paper focuses on a study methods for hiding encrypted information, specifically, methods that encrypt data before embedding in host data where the ‘data’ is in the form of a full colour digital image. Such methods provide a greater level of data security especially when the information is to be submitted over the Internet, for example, since a potential attacker needs to first detect, then extract and then decrypt the embedded data in order to recover the original information. After providing an extensive survey of the current methods available, we present a new method of encrypting and then hiding full colour images in three full colour host images with out loss of fidelity following data extraction and decryption. The application of this technique, which is based on a technique called ‘Stochastic Diffusion’ are wide ranging and include covert image information interchange, digital image authentication, video authentication, copyright protection and digital rights management of image data in general

A Covert Encryption Method for Applications in Electronic Data Interchange

A principal weakness of all encryption systems is that the output data can be ‘seen’ to be encrypted. In other words, encrypted data provides a ‘flag’ on the potential value of the information that has been encrypted. In this paper, we provide a new approach to ‘hiding’ encrypted data in a digital image. In conventional (symmetric) encryption, the plaintext is usually represented as a binary stream and encrypted using an XOR type operation with a binary cipher. The algorithm used is ideally designed to: (i) generate a maximum entropy cipher so that there is no bias with regard to any bit; (ii) maximize diffusion in terms of key dependency so that a change in any bit of the key can effect any, and potentially all, bits of the cipher. In the work reported here, we consider an approach in which a binary or low-bit plaintext image is encrypted with a decimal integer or floating point cipher using a convolution operation and the output quantized into a 1-bit array generating a binary image ciphertext. This output is then ‘embedded’ in a host image to hide the encrypted information. Embedding is undertaken either in the lowest 1-bit layer or multiple 1-bit layers. Decryption is accomplished by: (i) extracting the binary image from the host image; (ii) correlating the result with the original cipher. In principle, any cipher generator can be used for this purpose and the method has been designed to operate with 24-bit colour images. The approach has a variety of applications and, in this paper, we focus on the authentication and self-authentication of e-documents (letters and certificates, for example) that are communicated over the Internet and are thereby vulnerable to attack (e.g. modification, editing, counterfeiting etc.). In addition to document authentication, the approach considered provides a way of propagating disinformation and a solution to scenarios that require ‘plausible deniability’