Functional signatures

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 41-42).In this thesis, we introduce the notion of functional signatures. In a functional signature scheme, in addition to a master signing key that can be used to sign any message, there are signing keys for a function f, which allow one to sign any message in the range of f. An immediate application of functional signature scheme is the delegation by a master authority to a third party of the ability to sign a restricted set of messages. We also show applications of functional signature in constructing succinct non-interactive arguments and delegation schemes. We give several constructions for this primitive, and describe the trade-offs between them in terms of the assumptions they require and the size of the signatures.by Ioana Ivan.S.M

Cryptographic Tools for Privacy Preservation

Data permeates every aspect of our daily life and it is the backbone of our digitalized society. Smartphones, smartwatches and many more smart devices measure, collect, modify and share data in what is known as the Internet of Things.Often, these devices don’t have enough computation power/storage space thus out-sourcing some aspects of the data management to the Cloud. Outsourcing computation/storage to a third party poses natural questions regarding the security and privacy of the shared sensitive data.Intuitively, Cryptography is a toolset of primitives/protocols of which security prop- erties are formally proven while Privacy typically captures additional social/legislative requirements that relate more to the concept of “trust” between people, “how” data is used and/or “who” has access to data. This thesis separates the concepts by introducing an abstract model that classifies data leaks into different types of breaches. Each class represents a specific requirement/goal related to cryptography, e.g. confidentiality or integrity, or related to privacy, e.g. liability, sensitive data management and more.The thesis contains cryptographic tools designed to provide privacy guarantees for different application scenarios. In more details, the thesis:(a) defines new encryption schemes that provide formal privacy guarantees such as theoretical privacy definitions like Differential Privacy (DP), or concrete privacy-oriented applications covered by existing regulations such as the European General Data Protection Regulation (GDPR);(b) proposes new tools and procedures for providing verifiable computation’s guarantees in concrete scenarios for post-quantum cryptography or generalisation of signature schemes;(c) proposes a methodology for utilising Machine Learning (ML) for analysing the effective security and privacy of a crypto-tool and, dually, proposes a secure primitive that allows computing specific ML algorithm in a privacy-preserving way;(d) provides an alternative protocol for secure communication between two parties, based on the idea of communicating in a periodically timed fashion

Automatic segmentation of relevant textures in agricultural images

One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevantimage processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processin

Techniques, Taxonomy, and Challenges of Privacy Protection in the Smart Grid

As the ease with which any data are collected and transmitted increases, more privacy concerns arise leading to an increasing need to protect and preserve it. Much of the recent high-profile coverage of data mishandling and public mis- leadings about various aspects of privacy exasperates the severity. The Smart Grid (SG) is no exception with its key characteristics aimed at supporting bi-directional information flow between the consumer of electricity and the utility provider. What makes the SG privacy even more challenging and intriguing is the fact that the very success of the initiative depends on the expanded data generation, sharing, and pro- cessing. In particular, the deployment of smart meters whereby energy consumption information can easily be collected leads to major public hesitations about the tech- nology. Thus, to successfully transition from the traditional Power Grid to the SG of the future, public concerns about their privacy must be explicitly addressed and fears must be allayed. Along these lines, this chapter introduces some of the privacy issues and problems in the domain of the SG, develops a unique taxonomy of some of the recently proposed privacy protecting solutions as well as some if the future privacy challenges that must be addressed in the future.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111644/1/Uludag2015SG-privacy_book-chapter.pd

Privacy in the Smart City - Applications, Technologies, Challenges and Solutions

Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities

A SECURED AUTHENTICATED WATERMARKING TECHNIQUE

Whenever media contents transmitted through the network, compressed and encrypted media data is used so it is important to give proper protection to the data items to avoid unauthorized access and for that we need to enhance media authentication and for that the compressed encrypted media data which is used to distribute through the network is watermarked for providing proof of ownership or distributorship. For doing compression JPEG 2000 compression and while doing compression the data is packed to  low  number of bits and to this data encryption is applied so  stream cipher technique is used for avoiding media quality degradation and also this technique allow to do watermarking in a predictable manner. And a robust watermarking algorithm is used for watermarking this compressed and encrypted media data

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

Image source identification and characterisation for forensic analysis

Digital imaging devices, such as digital cameras or mobile phones, are prevalent in society. The images created by these devices can be used in the commission of crime. Source device identification is an emerging research area and involves the identification of artefacts that are left behind in an image by the camera pipeline. These artefacts can be used as digital signatures to identify the source device forensically. The type of digital signature considered in this thesis is the Sensor Pattern Noise (SPN), which consists mainly of the PRNU (Photo Response Non-Uniformity) of the imaging device. The PRNU is unique to each individual sensor, which can be extracted traditionally with a wavelet denoising filter and enhanced to attenuate unwanted artefacts. This thesis proposes a novel method to extract the PRNU of a digital image by using Singular Value Decomposition (SVD) to extract the digital signature. The extraction of the PRNU is performed using the homomorphic filtering technique, where the inherently nonlinear PRNU is transformed into an additive noise. The range of the energy of the PRNU is estimated, which makes it easier to separate from other polluting components to obtain a cleaner signature, as compared to extracting all the high frequency signals from an image. The image is decomposed by using SVD, which separates the image into ranks of descending order of energies. The estimated energy range of the PRNU is used to obtain the interesting ranks that are utilised to form part of the digital signature. A case study of an existing image analyser platform was performed by investigating its identification and classification results. The SVD based extraction method was tested by extracting image signatures from camera phones. The results of the experiments show that it is possible to determine the source device of digital images

A compiler for multi-key homomorphic signatures for Turing machines

At SCN 2018, Fiore and Pagnin proposed a generic compiler (called ``Matrioska\u27\u27) allowing to transform sufficiently expressive single-key homomorphic signatures (SKHSs) into multi-key homomorphic signatures (MKHSs) under falsifiable assumptions in the standard model. Matrioska is designed for homomorphic signatures that support programs represented as circuits. The MKHS schemes obtained through Matrioska support the evaluation and verification of arbitrary circuits over data signed from multiple users, but they require the underlying SKHS scheme to work with circuits whose size is exponential in the number of users, and thus can only support a constant number of users. In this work, we propose a new generic compiler to convert an SKHS scheme into an MKHS scheme. Our compiler is a generalization of Matrioska for homomorphic signatures that support programs in any model of computation. When instantiated with SKHS for circuits, we recover the Matrioska compiler of Fiore and Pagnin. As an additional contribution, we show how to instantiate our generic compiler in the Turing Machines (TM) model and argue that this instantiation allows to overcome some limitations of Matrioska: - First, the MKHS we obtain require the underlying SKHS to support TMs whose size depends only linearly in the number of users. - Second, when instantiated with an SKHS with succinctness $poly(\lambda)$ and fast enough verification time, e.g., $S \cdot \log T + n \cdot poly(\lambda)$ or $T +n \cdot poly(\lambda)$ (where $T$, $S$, and $n$ are the running time, description size, and input length of the program to verify, respectively), our compiler yields an MKHS in which the time complexity of both the prover and the verifier remains $poly(\lambda)$ even if executed on programs with inputs from $poly(\lambda)$ users. While we leave constructing an SKHS with these efficiency properties as an open problem, we make one step towards this goal by proposing an SKHS scheme with verification time $poly(\lambda) \cdot T$ under falsifiable assumptions in the standard model

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