Biblio

Now a day, need for fast accessing of data is increasing with the exponential increase in the security field. QR codes have served as a useful tool for fast and convenient sharing of data. But with increased usage of QR Codes have become vulnerable to attacks such as phishing, pharming, manipulation and exploitation. These security flaws could pose a danger to an average user. In this paper we have proposed a way, called Secured QR (SQR) to fix all these issues. In this approach we secure a QR code with the help of a key in generator side and the same key is used to get the original information at scanner side. We have used AES algorithm for this purpose. SQR approach is applicable when we want to share/use sensitive information in the organization such as sharing of profile details, exchange of payment information, business cards, generation of electronic tickets etc.

As QR codes become ubiquitous, there is a prominent security threat of phishing and malware attacks that can be carried out by sharing rogue URLs through such codes. Several QR code scanner apps have become available in the past few years to combat such threats. Nevertheless, limited work exists in the literature evaluating such apps in the context of security. In this paper, we have investigated the status of existing secure QR code scanner apps for Android from a security point of view. We found that several of the so-called secure QR code scanner apps merely present the URL encoded in a QR code to the user rather than validating it against suitable threat databases. Further, many apps do not support basic security features such as displaying the URL to the user and asking for user confirmation before proceeding to open the URL in a browser. The most alarming issue that emerged during this study is that only two of the studied apps perform validation of the redirected URL associated with a QR code. We also tested the relevant apps with a set of benign, phishing and malware URLs collected from multiple sources. Overall, the results of our experiments imply that the protection offered by the examined secure QR code scanner apps against rogue URLs (especially malware URLs) is limited. Based on the findings of our investigation, we have distilled a set of key lessons and proposed design recommendations to enhance the security aspects of such apps.

Smart Card has complications with validation and transmission process. Therefore, by using peeping attack, the secret code was stolen and secret filming while entering Personal Identification Number at the ATM machine. We intend to develop an authentication system to banks that protects the asset of user's. The data of a user is to be ensured that secure and isolated from the data leakage and other attacks Therefore, we propose a system, where ATM machine will have a QR code in which the information's are encrypted corresponding to the ATM machine and a mobile application in the customer's mobile which will decrypt the encoded QR information and sends the information to the server and user's details are displayed in the ATM machine and transaction can be done. Now, the user securely enters information to transfer money without risk of peeping attack in Automated Teller Machine by just scanning the QR code at the ATM by mobile application. Here, both the encryption and decryption technique are carried out by using Triple DES Algorithm (Data Encryption Standard).

Internet of Things (IoT) devices are getting increasingly popular, becoming a core element for the next generations of informational architectures: smart city, smart factory, smart home, smart health-care and many others. IoT systems are mainly comprised of embedded devices with limited computing capabilities while having a cloud component which processes the data and delivers it to the end-users. IoT devices access the user private data, thus requiring robust security solution which must address features like usability and scalability. In this paper we discuss about an IoT authentication service for smart-home devices using a smart-phone as security anchor, QR codes and attribute based cryptography (ABC). Regarding the fact that in an IoT ecosystem some of the IoT devices and the cloud components may be considered untrusted, we propose a privacy preserving attribute based access control protocol to handle the device authentication to the cloud service. For the smart-phone centric authentication to the cloud component, we employ the FIDO UAF protocol and we extend it, by adding an attributed based privacy preserving component.

The utilization of the online services especially the access to Internet Banking services has grown rapidly from last five years. The Internet Banking services provide the customers with the secure and reliable environment to deal with. But with the technology advancement, it is mandatory for the banks to put into practice the ideal technologies or the best security strategies and procedures to authorize or validate the originality of the customers. This must be done to ensure that the data or the information being transmitted during any kind of transaction is safe and no kind of leakage or modification of the information is possible for the intruder. This paper presents a digital watermark method for the QR Code (Quick Response Code) In this, a visible watermark is embedded in the QR Code image using the watermark technology (DCT) and describes the functioning feature of a secure authorization system by means of QR codes & the digital watermark for Internet Banking.

The revolution of smart devices has a significant and positive impact on the lives of many people, especially in regard to elements of healthcare. In part, this revolution is attributed to technological advances that enable individuals to wear and use medical devices to monitor their health activities, but remotely. Also, these smart, wearable medical devices assist health care providers in monitoring their patients remotely, thereby enabling physicians to respond quickly in the event of emergencies. An ancillary advantage is that health care costs will be reduced, another benefit that, when paired with prompt medical treatment, indicates significant advances in the contemporary management of health care. However, the competition among manufacturers of these medical devices creates a complexity of small and smart wearable devices such as ECG and EMG. This complexity results in other issues such as patient security, privacy, confidentiality, and identity theft. In this paper, we discuss the design and implementation of a hybrid real-time cryptography algorithm to secure lightweight wearable medical devices. The proposed system is based on an emerging innovative technology between the genomic encryptions and the deterministic chaos method to provide a quick and secure cryptography algorithm for real-time health monitoring that permits for threats to patient confidentiality to be addressed. The proposed algorithm also considers the limitations of memory and size of the wearable health devices. The experimental results and the encryption analysis indicate that the proposed algorithm provides a high level of security for the remote health monitoring system.

Applying security to the transmitted image is very important issues, because the transmission channel is open and can be compromised by attackers. To secure this channel from the eavesdropping attack, man in the middle attack, and so on. A new hybrid encryption image mechanism that utilize triangular scrambling, DNA encoding and chaotic map is implemented. The scheme takes a master key with a length of 320 bit, and produces a group of sub-keys with two length (32 and 128 bit) to encrypt the blocks of images, then a new triangular scrambling method is used to increase the security of the image. Many experiments are implemented using several different images. The analysis results for these experiments show that the security obtained on by using the proposed method is very suitable for securing the transmitted images. The current work has been compared with other works and the result of comparison shows that the current work is very strong against attacks.

Personalized medicine performs diagnoses and treatments according to the DNA information of the patients. The new paradigm will change the health care model in the future. A doctor will perform the DNA sequence matching instead of the regular clinical laboratory tests to diagnose and medicate the diseases. Additionally, with the help of the affordable personal genomics services such as 23andMe, personalized medicine will be applied to a great population. Cloud computing will be the perfect computing model as the volume of the DNA data and the computation over it are often immense. However, due to the sensitivity, the DNA data should be encrypted before being outsourced into the cloud. In this paper, we start from a practical system model of the personalize medicine and present a solution for the secure DNA sequence matching problem in cloud computing. Comparing with the existing solutions, our scheme protects the DNA data privacy as well as the search pattern to provide a better privacy guarantee. We have proved that our scheme is secure under the well-defined cryptographic assumption, i.e., the sub-group decision assumption over a bilinear group. Unlike the existing interactive schemes, our scheme requires only one round of communication, which is critical in practical application scenarios. We also carry out a simulation study using the real-world DNA data to evaluate the performance of our scheme. The simulation results show that the computation overhead for real world problems is practical, and the communication cost is small. Furthermore, our scheme is not limited to the genome matching problem but it applies to general privacy preserving pattern matching problems which is widely used in real world.

Since the first whole-genome sequencing, the biomedical research community has made significant steps towards a more precise, predictive and personalized medicine. Genomic data is nowadays widely considered privacy-sensitive and consequently protected by strict regulations and released only after careful consideration. Various additional types of biomedical data, however, are not shielded by any dedicated legal means and consequently disseminated much less thoughtfully. This in particular holds true for DNA methylation data as one of the most important and well-understood epigenetic element influencing human health. In this paper, we show that, in contrast to the aforementioned belief, releasing one's DNA methylation data causes privacy issues akin to releasing one's actual genome. We show that already a small subset of methylation regions influenced by genomic variants are sufficient to infer parts of someone's genome, and to further map this DNA methylation profile to the corresponding genome. Notably, we show that such re-identification is possible with 97.5% accuracy, relying on a dataset of more than 2500 genomes, and that we can reject all wrongly matched genomes using an appropriate statistical test. We provide means for countering this threat by proposing a novel cryptographic scheme for privately classifying tumors that enables a privacy-respecting medical diagnosis in a common clinical setting. The scheme relies on a combination of random forests and homomorphic encryption, and it is proven secure in the honest-but-curious model. We evaluate this scheme on real DNA methylation data, and show that we can keep the computational overhead to acceptable values for our application scenario.

DNA cryptography is one of the promising fields in cryptographic research which emerged with the evolution of DNA computing. In this era, end to end transmission of secure data by ensuring confidentiality and authenticity over the networks is a real challenge. Even though various DNA based cryptographic algorithms exists, they are not secure enough to provide better security as required with today's security requirements. Hence we propose a cryptographic model which will enhance the message security. A new method of round key selection is used, which provides better and enhanced security against intruder's attack. The crucial attraction of this proposed model is providing multi level security of 3 levels with round key selection and message encryption in level 1, 16×16 matrix manipulation using asymmetric key encryption in level 2 and shift operations in level 3. Thus we design a system with multi level encryption without compromising complexity and size of the cipher text.

Cellular Automata based computing paradigm is an efficient platform for modeling complicated computational problems. This can be used for various applications in the field of Cryptography. In this paper, it is used for generating a DNA cryptography based encryption algorithm. The encoded message in binary format is encrypted to cipher colors with the help of a simple algorithm based on the principles of DNA cryptography and cellular automata. The message will be in compressed form using XOR operator. Since cellular automata and DNA cryptographic principles are exploited, high level of parallelism, reversibility, uniformity etc. can be achieved.

In today's growing concern for home security, we have developed an advanced security system using integrated digital signature and DNA cryptography. The digital signature is formed using multi-feature biometric traits which includes both fingerprint as well as iris image. We further increase the security by using DNA cryptography which is embedded on a smart card. In order to prevent unauthorized access manually or digitally, we use geo-detection which compares the unregistered devices location with the user's location using any of their personal devices such as smart phone or tab.

The proposed frame describes two objectives one is to issue certificates through online and second is provide three level security through DNA cryptography. DNA Cryptography means converting the data to the DNA sequence. DNA is a succession comprising of four letters in order; A, C, G and T. every letter set is identified with a nucleotide. DNA can be used for store data, transmit the data and also used for computation of the data. This paper implemented 3 levels of cryptography. The receiver will apply the decryption for extracting the readable from the unreadable format. This DNA cryptography provide the security more than the other cryptography, but it takes more time complexity for generating the encoding and decoding and it has the chances to hacking the data by the hacker. So in this paper we implement the fast three level DNA Cryptography for me seva services.

The data security is a challenging issue nowadays with the increase of information capacity and its transmission rate. The most common and widely used techniques in the data security fields are cryptography and steganography. The combination of cryptography and steganography methods provides more security to the data. Now, DNA (Deoxyribonucleic Acid) is explored as a new carrier for data security since it achieves maximum protection and powerful security with high capacity and low modification rate. A new data security method can be developed by taking the advantages of DNA based AES (Advanced Encryption Standard) cryptography and DNA steganography. This new technique will provide multilayer security to the secret message. Here the secret message is first encoded to DNA bases then DNA based AES algorithm is applied to it. Finally the encrypted DNA will be concealed in another DNA sequence. This hybrid technique provides triple layer security to the secret message.

This article presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable emcient and accurate computation of a target's location while preserving the privacy of the observers.

Homomorphic encryption technology can settle a dispute of data privacy security in cloud environment, but there are many problems in the process of access the data which is encrypted by a homomorphic algorithm in the cloud. In this paper, on the premise of attribute encryption, we propose a fully homomorphic encrypt scheme which based on attribute encryption with LSSS matrix. This scheme supports fine-grained cum flexible access control along with "Query-Response" mechanism to enable users to efficiently retrieve desired data from cloud servers. In addition, the scheme should support considerable flexibility to revoke system privileges from users without updating the key client, it reduces the pressure of the client greatly. Finally, security analysis illustrates that the scheme can resist collusion attack. A comparison of the performance from existing CP-ABE scheme, indicates that our scheme reduces the computation cost greatly for users.

Genetic data are important dataset utilised in genetic epidemiology to investigate biologically coded information within the human genome. Enormous research has been delved into in recent years in order to fully sequence and understand the genome. Personalised medicine, patient response to treatments and relationships between specific genes and certain characteristics such as phenotypes and diseases, are positive impacts of studying the genome, just to mention a few. The sensitivity, longevity and non-modifiable nature of genetic data make it even more interesting, consequently, the security and privacy for the storage and processing of genomic data beg for attention. A common activity carried out by geneticists is the association analysis between allele-allele, or even a genetic locus and a disease. We demonstrate the use of cryptographic techniques such as homomorphic encryption schemes and multiparty computations, how such analysis can be carried out in a privacy friendly manner. We compute a 3 × 3 contingency table, and then, genome analyses algorithms such as linkage disequilibrium (LD) measures, all on the encrypted domain. Our computation guarantees privacy of the genome data under our security settings, and provides up to 98.4% improvement, compared to an existing solution.

Homomorphic signatures can provide a credential of a result which is indeed computed with a given function on a data set by an untrusted third party like a cloud server, when the input data are stored with the signatures beforehand. Boneh and Freeman in EUROCRYPT2011 proposed a homomorphic signature scheme for polynomial functions of any degree, however the scheme is not based on the normal short integer solution (SIS) problems as its security assumption. In this paper, we show a homomorphic signature scheme for quadratic polynomial functions those security assumption is based on the normal SIS problems. Our scheme constructs the signatures of multiplication as tensor products of the original signature vectors of input data so that homomorphism holds. Moreover, security of our scheme is reduced to the hardness of the SIS problems respect to the moduli such that one modulus is the power of the other modulus. We show the reduction by constructing solvers of the SIS problems respect to either of the moduli from any forger of our scheme.

We prove polarization theorems for arbitrary classical-quantum (cq) channels. The input alphabet is endowed with an arbitrary Abelian group operation and an Arikan-style transformation is applied using this operation. It is shown that as the number of polarization steps becomes large, the synthetic cq-channels polarize to deterministic homomorphism channels that project their input to a quotient group of the input alphabet. This result is used to construct polar codes for arbitrary cq-channels and arbitrary classical-quantum multiple access channels (cq-MAC). The encoder can be implemented in O(N log N) operations, where N is the blocklength of the code. A quantum successive cancellation decoder for the constructed codes is proposed. It is shown that the probability of error of this decoder decays faster than 2-Nβ for any β textless; ½.

The Internet of Things is a disruptive paradigm based on the cooperation of a plethora of heterogeneous smart things to collect, transmit, and analyze data from the ambient environment. To this end, many monitored variables are combined by a data analysis module in order to implement efficient context-aware decision mechanisms. To ensure resource efficiency, aggregation is a long established solution, however it is applicable only in the case of one sensed variable. We extend the use of aggregation to the complex context of IoT by proposing a novel approach for secure cooperation of smart things while granting confidentiality and integrity. Traditional solutions for data concealment in resource constrained devices rely on hop-by-hop or end-to-end encryption, which are shown to be inefficient in our context. We use a more sophisticated scheme relying on homomorphic encryption which is not compromise resilient. We combine fully additive encryption with fully additive secret sharing to fulfill the required properties. Thorough security analysis and performance evaluation show a viable tradeoff between security and efficiency for our scheme.

We consider the problem of privacy-preserving data aggregation in a star network topology, i.e., several untrusting participants connected to a single aggregator. We require that the participants do not discover each other's data, and the service provider remains oblivious to each participant's individual contribution. Furthermore, the final result is to be published in a differentially private manner, i.e., the result should not reveal the contribution of any single participant to a (possibly external) adversary who knows the contributions of all other participants. In other words, we require a secure multiparty computation protocol that also incorporates a differentially private mechanism. Previous solutions have resorted to caveats such as postulating a trusted dealer to distribute keys to the participants, or introducing additional entities to withhold the decryption key from the aggregator, or relaxing the star topology by allowing pairwise communication amongst the participants. In this paper, we show how to obtain a noisy (differentially private) aggregation result using Shamir secret sharing and additively homomorphic encryption without these mitigating assumptions. More importantly, while we assume semi-honest participants, we allow the aggregator to be stronger than semi-honest, specifically in the sense that he can try to reduce the noise in the differentially private result. To respect the differential privacy requirement, collusions of mutually untrusting entities need to be analyzed differently from traditional secure multiparty computation: It is not sufficient that such collusions do not reveal the data of honest participants; we must also ensure that the colluding entities cannot undermine differential privacy by reducing the amount of noise in the final result. Our protocols avoid this by requiring that no entity – neither the aggregator nor any participant – knows how much noise a participant contributes to the final result. We also ensure that if a cheating aggregator tries to influence the noise term in the differentially private output, he can be detected with overwhelming probability.

Machine learning algorithms based on deep neural networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand, with increasing growth of cloud services, several Machine Learning as a Service (MLaaS) are offered where training and deploying machine learning models are performed on cloud providers' infrastructure. However, machine learning algorithms require access to raw data which is often privacy sensitive and can create potential security and privacy risks. To address this issue, we develop new techniques to provide solutions for applying deep neural network algorithms to the encrypted data. In this paper, we show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. We demonstrate applicability of the proposed techniques and evaluate its performance. The empirical results show that it provides accurate privacy-preserving training and classification.

Private Set Intersection (PSI) is a cryptographic technique that allows two parties to compute the intersection of their sets without revealing anything except the intersection. We use fully homomorphic encryption to construct a fast PSI protocol with a small communication overhead that works particularly well when one of the two sets is much smaller than the other, and is secure against semi-honest adversaries. The most computationally efficient PSI protocols have been constructed using tools such as hash functions and oblivious transfer, but a potential limitation with these approaches is the communication complexity, which scales linearly with the size of the larger set. This is of particular concern when performing PSI between a constrained device (cellphone) holding a small set, and a large service provider (e.g. WhatsApp), such as in the Private Contact Discovery application. Our protocol has communication complexity linear in the size of the smaller set, and logarithmic in the larger set. More precisely, if the set sizes are Ny textless Nx, we achieve a communication overhead of O(Ny log Nx). Our running-time-optimized benchmarks show that it takes 36 seconds of online-computation, 71 seconds of non-interactive (receiver-independent) pre-processing, and only 12.5MB of round trip communication to intersect five thousand 32-bit strings with 16 million 32-bit strings. Compared to prior works, this is roughly a 38–115x reduction in communication with minimal difference in computational overhead.

Let us consider a scenario that a data holder (e.g., a hospital) encrypts a data (e.g., a medical record) which relates a keyword (e.g., a disease name), and sends its ciphertext to a server. We here suppose not only the data but also the keyword should be kept private. A receiver sends a query to the server (e.g., average of body weights of cancer patients). Then, the server performs the homomorphic operation to the ciphertexts of the corresponding medical records, and returns the resultant ciphertext. In this scenario, the server should NOT be allowed to perform the homomorphic operation against ciphertexts associated with different keywords. If such a mis-operation happens, then medical records of different diseases are unexpectedly mixed. However, in the conventional homomorphic encryption, there is no way to prevent such an unexpected homomorphic operation, and this fact may become visible after decrypting a ciphertext, or as the most serious case it might be never detected. To circumvent this problem, in this paper, we propose mis-operation resistant homomorphic encryption, where even if one performs the homomorphic operations against ciphertexts associated with keywords ω' and ω, where ω -ω', the evaluation algorithm detects this fact. Moreover, even if one (intentionally or accidentally) performs the homomorphic operations against such ciphertexts, a ciphertext associated with a random keyword is generated, and the decryption algorithm rejects it. So, the receiver can recognize such a mis-operation happens in the evaluation phase. In addition to mis-operation resistance, we additionally adopt secure search functionality for keywords since it is desirable when one would like to delegate homomorphic operations to a third party. So, we call the proposed primitive mis-operation resistant searchable homomorphic encryption (MR-SHE). We also give our implementation result of inner products of encrypted vectors. In the case when both vectors are encrypted, the running time of the receiver is millisecond order for relatively small-dimensional (e.g., 26) vectors. In the case when one vector is encrypted, the running time of the receiver is approximately 5 msec even for relatively high-dimensional (e.g., 213) vectors.

We continue the study of Homomorphic Secret Sharing (HSS), recently introduced by Boyle et al. (Crypto 2016, Eurocrypt 2017). A (2-party) HSS scheme splits an input x into shares (x0,x1) such that (1) each share computationally hides x, and (2) there exists an efficient homomorphic evaluation algorithm \$\textbackslashEval\$ such that for any function (or "program") from a given class it holds that Eval(x0,P)+Eval(x1,P)=P(x). Boyle et al. show how to construct an HSS scheme for branching programs, with an inverse polynomial error, using discrete-log type assumptions such as DDH. We make two types of contributions. Optimizations. We introduce new optimizations that speed up the previous optimized implementation of Boyle et al. by more than a factor of 30, significantly reduce the share size, and reduce the rate of leakage induced by selective failure. Applications. Our optimizations are motivated by the observation that there are natural application scenarios in which HSS is useful even when applied to simple computations on short inputs. We demonstrate the practical feasibility of our HSS implementation in the context of such applications.