Watermarking security part I: theory

This article proposes a theory of watermarking security based on a cryptanalysis point of view. The main idea is that information about the secret key leaks from the observations, for instance watermarked pieces of content, available to the opponent. Tools from information theory (Shannon's mutual information and Fisher's information matrix) can measure this leakage of information. The security level is then defined as the number of observations the attacker needs to successfully estimate the secret key. This theory is applied to common watermarking methods: the substitutive scheme and spread spectrum based techniques. Their security levels are calculated against three kinds of attack

Dynamic block encryption with self-authenticating key exchange

One of the greatest challenges facing cryptographers is the mechanism used for key exchange. When secret data is transmitted, the chances are that there may be an attacker who will try to intercept and decrypt the message. Having done so, he/she might just gain advantage over the information obtained, or attempt to tamper with the message, and thus, misguiding the recipient. Both cases are equally fatal and may cause great harm as a consequence. In cryptography, there are two commonly used methods of exchanging secret keys between parties. In the first method, symmetric cryptography, the key is sent in advance, over some secure channel, which only the intended recipient can read. The second method of key sharing is by using a public key exchange method, where each party has a private and public key, a public key is shared and a private key is kept locally. In both cases, keys are exchanged between two parties. In this thesis, we propose a method whereby the risk of exchanging keys is minimised. The key is embedded in the encrypted text using a process that we call `chirp coding', and recovered by the recipient using a process that is based on correlation. The `chirp coding parameters' are exchanged between users by employing a USB flash memory retained by each user. If the keys are compromised they are still not usable because an attacker can only have access to part of the key. Alternatively, the software can be configured to operate in a one time parameter mode, in this mode, the parameters are agreed upon in advance. There is no parameter exchange during file transmission, except, of course, the key embedded in ciphertext. The thesis also introduces a method of encryption which utilises dynamic blocks, where the block size is different for each block. Prime numbers are used to drive two random number generators: a Linear Congruential Generator (LCG) which takes in the seed and initialises the system and a Blum-Blum Shum (BBS) generator which is used to generate random streams to encrypt messages, images or video clips for example. In each case, the key created is text dependent and therefore will change as each message is sent. The scheme presented in this research is composed of five basic modules. The first module is the key generation module, where the key to be generated is message dependent. The second module, encryption module, performs data encryption. The third module, key exchange module, embeds the key into the encrypted text. Once this is done, the message is transmitted and the recipient uses the key extraction module to retrieve the key and finally the decryption module is executed to decrypt the message and authenticate it. In addition, the message may be compressed before encryption and decompressed by the recipient after decryption using standard compression tools

Privacy-preserving information hiding and its applications

The phenomenal advances in cloud computing technology have raised concerns about data privacy. Aided by the modern cryptographic techniques such as homomorphic encryption, it has become possible to carry out computations in the encrypted domain and process data without compromising information privacy. In this thesis, we study various classes of privacy-preserving information hiding schemes and their real-world applications for cyber security, cloud computing, Internet of things, etc. Data breach is recognised as one of the most dreadful cyber security threats in which private data is copied, transmitted, viewed, stolen or used by unauthorised parties. Although encryption can obfuscate private information against unauthorised viewing, it may not stop data from illegitimate exportation. Privacy-preserving Information hiding can serve as a potential solution to this issue in such a manner that a permission code is embedded into the encrypted data and can be detected when transmissions occur. Digital watermarking is a technique that has been used for a wide range of intriguing applications such as data authentication and ownership identification. However, some of the algorithms are proprietary intellectual properties and thus the availability to the general public is rather limited. A possible solution is to outsource the task of watermarking to an authorised cloud service provider, that has legitimate right to execute the algorithms as well as high computational capacity. Privacypreserving Information hiding is well suited to this scenario since it is operated in the encrypted domain and hence prevents private data from being collected by the cloud. Internet of things is a promising technology to healthcare industry. A common framework consists of wearable equipments for monitoring the health status of an individual, a local gateway device for aggregating the data, and a cloud server for storing and analysing the data. However, there are risks that an adversary may attempt to eavesdrop the wireless communication, attack the gateway device or even access to the cloud server. Hence, it is desirable to produce and encrypt the data simultaneously and incorporate secret sharing schemes to realise access control. Privacy-preserving secret sharing is a novel research for fulfilling this function. In summary, this thesis presents novel schemes and algorithms, including: • two privacy-preserving reversible information hiding schemes based upon symmetric cryptography using arithmetic of quadratic residues and lexicographic permutations, respectively. • two privacy-preserving reversible information hiding schemes based upon asymmetric cryptography using multiplicative and additive privacy homomorphisms, respectively. • four predictive models for assisting the removal of distortions inflicted by information hiding based respectively upon projection theorem, image gradient, total variation denoising, and Bayesian inference. • three privacy-preserving secret sharing algorithms with different levels of generality

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Secure and Privacy-preserving Data Sharing in the Cloud based on Lossless Image Coding

Abstract Image and video processing in the encrypted domain has recently emerged as a promising research area to tackle privacy-related data processing issues. In particular, reversible data hiding in the encrypted domain has been suggested as a solution to store and manage digital images securely in the cloud while preserving their confidentiality. However, although efficiency has been claimed with reversible data hiding techniques in encrypted images (RDHEI), reported results show that the cloud service provider cannot add more than 1 bit per pixel (bpp) of additional data to manage stored images. This paper highlights the weakness of RDHEI as a suggested approach for secure and privacy-preserving cloud computing. In particular, we propose a new, simple, and efficient approach that offers the same level of data security and confidentiality in the cloud without the process of reversible data hiding. The proposed idea is to compress the image via a lossless image coder in order to create space before encryption. This space is then filled with a randomly generated sequence and combined with an encrypted version of the compressed bit stream to form a full resolution encrypted image in the pixel domain. The cloud service provider uses the created room in the encrypted image to add additional data and produces an encrypted image containing additional data in a similar fashion. Assessed with the lossless Embedded Block Coding with Optimized Truncation (EBCOT) algorithm on natural images, the proposed scheme has been shown to exceed the capacity of 3 bpp of additional data while maintaining data security and confidentiality

Cryptography and Its Applications in Information Security

Nowadays, mankind is living in a cyber world. Modern technologies involve fast communication links between potentially billions of devices through complex networks (satellite, mobile phone, Internet, Internet of Things (IoT), etc.). The main concern posed by these entangled complex networks is their protection against passive and active attacks that could compromise public security (sabotage, espionage, cyber-terrorism) and privacy. This Special Issue “Cryptography and Its Applications in Information Security” addresses the range of problems related to the security of information in networks and multimedia communications and to bring together researchers, practitioners, and industrials interested by such questions. It consists of eight peer-reviewed papers, however easily understandable, that cover a range of subjects and applications related security of information

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Understanding and controlling leakage in machine learning

Machine learning models are being increasingly adopted in a variety of real-world scenarios. However, the privacy and confidentiality implications introduced in these scenarios are not well understood. Towards better understanding such implications, we focus on scenarios involving interactions between numerous parties prior to, during, and after training relevant models. Central to these interactions is sharing information for a purpose e.g., contributing data samples towards a dataset, returning predictions via an API. This thesis takes a step toward understanding and controlling leakage of private information during such interactions. In the first part of the thesis we investigate leakage of private information in visual data and specifically, photos representative of content shared on social networks. There is a long line of work to tackle leakage of personally identifiable information in social photos, especially using face- and body-level visual cues. However, we argue this presents only a narrow perspective as images reveal a wide spectrum of multimodal private information (e.g., disabilities, name-tags). Consequently, we work towards a Visual Privacy Advisor that aims to holistically identify and mitigate private risks when sharing social photos. In the second part, we address leakage during training of ML models. We observe learning algorithms are being increasingly used to train models on rich decentralized datasets e.g., personal data on numerous mobile devices. In such cases, information in the form of high-dimensional model parameter updates are anonymously aggregated from participating individuals. However, we find that the updates encode sufficient identifiable information and allows them to be linked back to participating individuals. We additionally propose methods to mitigate this leakage while maintaining high utility of the updates. In the third part, we discuss leakage of confidential information during inference time of black-box models. In particular, we find models lend themselves to model functionality stealing attacks: an adversary can interact with the black-box model towards creating a replica `knock-off' model that exhibits similar test-set performances. As such attacks pose a severe threat to the intellectual property of the model owner, we also work towards effective defenses. Our defense strategy by introducing bounded and controlled perturbations to predictions can significantly amplify model stealing attackers' error rates. In summary, this thesis advances understanding of privacy leakage when information is shared in raw visual forms, during training of models, and at inference time when deployed as black-boxes. In each of the cases, we further propose techniques to mitigate leakage of information to enable wide-spread adoption of techniques in real-world scenarios.Modelle für maschinelles Lernen werden zunehmend in einer Vielzahl realer Szenarien eingesetzt. Die in diesen Szenarien vorgestellten Auswirkungen auf Datenschutz und Vertraulichkeit wurden jedoch nicht vollständig untersucht. Um solche Implikationen besser zu verstehen, konzentrieren wir uns auf Szenarien, die Interaktionen zwischen mehreren Parteien vor, während und nach dem Training relevanter Modelle beinhalten. Das Teilen von Informationen für einen Zweck, z. B. das Einbringen von Datenproben in einen Datensatz oder die Rückgabe von Vorhersagen über eine API, ist zentral für diese Interaktionen. Diese Arbeit verhilft zu einem besseren Verständnis und zur Kontrolle des Verlusts privater Informationen während solcher Interaktionen. Im ersten Teil dieser Arbeit untersuchen wir den Verlust privater Informationen bei visuellen Daten und insbesondere bei Fotos, die für Inhalte repräsentativ sind, die in sozialen Netzwerken geteilt werden. Es gibt eine lange Reihe von Arbeiten, die das Problem des Verlustes persönlich identifizierbarer Informationen in sozialen Fotos angehen, insbesondere mithilfe visueller Hinweise auf Gesichts- und Körperebene. Wir argumentieren jedoch, dass dies nur eine enge Perspektive darstellt, da Bilder ein breites Spektrum multimodaler privater Informationen (z. B. Behinderungen, Namensschilder) offenbaren. Aus diesem Grund arbeiten wir auf einen Visual Privacy Advisor hin, der darauf abzielt, private Risiken beim Teilen sozialer Fotos ganzheitlich zu identifizieren und zu minimieren. Im zweiten Teil befassen wir uns mit Datenverlusten während des Trainings von ML-Modellen. Wir beobachten, dass zunehmend Lernalgorithmen verwendet werden, um Modelle auf umfangreichen dezentralen Datensätzen zu trainieren, z. B. persönlichen Daten auf zahlreichen Mobilgeräten. In solchen Fällen werden Informationen von teilnehmenden Personen in Form von hochdimensionalen Modellparameteraktualisierungen anonym verbunden. Wir stellen jedoch fest, dass die Aktualisierungen ausreichend identifizierbare Informationen codieren und es ermöglichen, sie mit teilnehmenden Personen zu verknüpfen. Wir schlagen zudem Methoden vor, um diesen Datenverlust zu verringern und gleichzeitig die hohe Nützlichkeit der Aktualisierungen zu erhalten. Im dritten Teil diskutieren wir den Verlust vertraulicher Informationen während der Inferenzzeit von Black-Box-Modellen. Insbesondere finden wir, dass sich Modelle für die Entwicklung von Angriffen, die auf Funktionalitätsdiebstahl abzielen, eignen: Ein Gegner kann mit dem Black-Box-Modell interagieren, um ein Replikat-Knock-Off-Modell zu erstellen, das ähnliche Test-Set-Leistungen aufweist. Da solche Angriffe eine ernsthafte Bedrohung für das geistige Eigentum des Modellbesitzers darstellen, arbeiten wir auch an einer wirksamen Verteidigung. Unsere Verteidigungsstrategie durch die Einführung begrenzter und kontrollierter Störungen in Vorhersagen kann die Fehlerraten von Modelldiebstahlangriffen erheblich verbessern. Zusammenfassend lässt sich sagen, dass diese Arbeit das Verständnis von Datenschutzverlusten beim Informationsaustausch verbessert, sei es bei rohen visuellen Formen, während des Trainings von Modellen oder während der Inferenzzeit von Black-Box-Modellen. In jedem Fall schlagen wir ferner Techniken zur Verringerung des Informationsverlusts vor, um eine weit verbreitete Anwendung von Techniken in realen Szenarien zu ermöglichen.Max Planck Institute for Informatic