What a lovely hat

Patient monitoring in hospitals, nursing centers, and home care can be largely automated using cameras and machine-learning-based video analytics, thus considerably increasing the efficiency of patient care. In particular, Facial-expression-based Pain Assessment Systems (FePAS) can automatically detect pain and notify medical personnel. However, current FePAS solutions using cloud-based video analytics offer very limited security and privacy protection. This is problematic, as video feeds of...

This article explores the potential of Secret Sharing-Based Internet of Things (SBIoT) as a promising cryptographic element across diverse applications, including secure data storage in commercial cloud systems (Datachest), smart home environments (encompassing sensors, cameras, smart locks, and smart assistants), and e-health applications (protecting patient data and medical records). Beyond these applications, the paper makes two key contributions: the introduction of a novel cheater...

Local Differential Privacy (LDP) mechanisms consist of (locally) adding controlled noise to data in order to protect the privacy of their owner. In this paper, we introduce a new cryptographic primitive called LDP commitment. Usually, a commitment ensures that the committed value cannot be modified before it is revealed. In the case of an LDP commitment, however, the value is revealed after being perturbed by an LDP mechanism. Opening an LDP commitment therefore requires a proof that the...

Clinical trials are crucial in the development of new medical treatment methods. To ensure the correctness of clinical trial results, medical institutes need to collect and process large volumes of participant data, which has prompted research on privacy preservation and data reliability. However, existing solutions struggle to resolve the trade-off between them due to the trust gap between the physical and digital worlds, limiting their practicality. To tackle the issues above, we present...

Internet of Medical Things (IoMT) has gained significant research focus in both academic and medical institutions. Nevertheless, the sensitive data involved in IoMT raises concerns regarding user validation and data privacy. To address these concerns, certificateless signcryption (CLSC) has emerged as a promising solution, offering authenticity, confidentiality, and unforgeability. Unfortunately, most existing CLSC schemes are impractical for IoMT due to their heavy computational and storage...

Machine learning (ML) as a service has emerged as a rapidly expanding field across various industries like healthcare, finance, marketing, retail and e-commerce, Industry 4.0, etc where a huge amount of data is gen- erated. To handle this amount of data, huge computational power is required for which cloud computing used to be the first choice. However, there are several challenges in cloud computing like limitations of bandwidth, network connectivity, higher latency, etc. To address...

This paper describes a way to protect medications against falsification, a long-standing problem in the world. We combine several existing technologies to achieve the stated goal. The building-blocks used are inherent physical randomness generated during the packaging process, artificial vision, short digital signatures and QR-codes.

Graph convolutional networks (GCNs) are gaining popularity due to their powerful modelling capabilities. However, guaranteeing privacy is an issue when evaluating on inputs that contain users’ sensitive information such as financial transactions, medical records, etc. To address such privacy concerns, we design Entrada, a framework for securely evaluating GCNs that relies on the technique of secure multiparty computation (MPC). For efficiency and accuracy reasons, Entrada builds over the MPC...

The classic stable matching algorithm of Gale and Shapley (American Mathematical Monthly '69) and subsequent variants such as those by Roth (Mathematics of Operations Research '82) and Abdulkadiroglu et al. (American Economic Review '05) have been used successfully in a number of real-world scenarios, including the assignment of medical-school graduates to residency programs, New York City teenagers to high schools, and Norwegian and Singaporean students to schools and universities. However,...

IoT devices collect privacy-sensitive data, e.g., in smart grids or in medical devices, and send this data to cloud servers for further processing. In order to ensure confidentiality as well as authenticity of the sensor data in the untrusted cloud environment, we consider a transciphering scenario between embedded IoT devices and multiple cloud servers that perform secure multi-party computation (MPC). Concretely, the IoT devices encrypt their data with a lightweight symmetric cipher and...

Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical data sets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their data sets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without...

Over the last few years, Internet of Medical Things (IoMT) has completely transformed the healthcare industry. It is bringing out the most notable, and unprecedented impacts on human health, and has totally changed the way we look at the healthcare industry. The healthcare sector all around the globe are leapfrogging, and adopting the technology, helping in transforming drastically in a very short span of time. However, as more and more number of medical devices are being connected to IoMT,...

Healthcare providers cannot share their patients' encrypted data among themselves because of interoperability issues. Many blockchain- based solutions have been proposed to allow for sharing medical data in a privacy-preserving manner, but interoperability problems persist. In this paper, we present a protocol called Blockchain-Format Preserving Encryption (B-FPE) to preserve patients' data privacy. Each patient is provided with an FPE key at the time of registration. All...

highlighting a looming cyber threat emanating from fast developing artificial intelligence. This strategic threat is further magnified with the advent of quantum computers. AI and quantum-AI (QAI) represent a totally new and effective vector of cryptanalytic attack. Much as modern AI successfully completes browser search phrases, so it is increasingly capable of guessing a rather narrow a-priori list of plausible plaintexts. This guessing is most effective over device cryptography where the...

The security of cryptographic primitives is an important issue. The Shor algorithm illustrates how quantum attacks threaten the security of these widely used primitives. Code-based cryptography is one of several approaches resistant to quantum attacks. To date, no attack has been able to break a code-based cryptosystem in polynomial time. Despite this level of security, these cryptosystems have not been considered for practical applications such as e-commerce, medical and industrial IoT,...

Electronic Medical Records (EMRs) have been utilized in plentiful medical institutions due to their superior convenience and low storage overhead. Nevertheless, it is difficult for medical departments with disparate management regulations to share EMRs through secure communication channels since sensitive EMRs are prone to be tampered with. Therefore, the EMRs should be encrypted before being outsourced to the network servers. Public key Encryption with Keyword Search (PEKS) has the ability...

The use of traditional cryptography based on symmetric keys has been replaced with the revolutionary idea discovered by Diffie and Hellman in 1976 that fundamentally changed communication systems by ensuring a secure transmission of information over an insecure channel. Nowadays public key cryptography is frequently used for authentication in e-commerce, digital signatures and encrypted communication. Most of the public key cryptosystems used in practice are based on integer factorization...

Embedded systems are a cornerstone of the ongoing digitization of our society, ranging from expanding markets around IoT and smart-X devices over to sensors in autonomous driving, medical equipment or critical infrastructures. Since a vast amount of embedded systems are safety-critical (e.g., due to their operation site), security is a necessity for their operation. However, unlike mobile, desktop, and server systems, where adversaries typically only act have remote access, embedded systems...

Threshold Fully Homomorphic Encryption (ThFHE) enables arbitrary computation over encrypted data while keeping the decryption key distributed across multiple parties at all times. ThFHE is a key enabler for threshold cryptography and, more generally, secure distributed computing. Existing ThFHE schemes relying on standard hardness assumptions, inherently require highly inefficient parameters and are unsuitable for practical deployment. In this paper, we take a novel approach towards making...

Random Allocation -the random assignment of the data to the parties- is a well-studied topic in the analysis of medical or judicial data, and the context of resource distribution. Random allocation reduces the chance of bias or corruption in the relevant applications, which makes the results more reliable. This is done by preventing a special or pre-planned assignment of the data to accommodate the assessment toward the desired results. This paper provides the first formal syntax and...

Due to the significant drop in prices for genome sequencing in the last decade, genome databases were constantly growing. This enabled genome analyses such as Genome-Wide Association Studies (GWAS) that study associations between a gene and a disease and allow to improve medical treatment. However, GWAS fails at the analysis of complex diseases caused by non-linear gene-gene interactions such as sporadic breast cancer or type 2 diabetes. Epistasis Analysis (EA) is a more powerful approach...

In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN...

Implantable Medical Devices (IMDs) are widely deployed today and often use wireless communication. Establishing a secure communication channel to these devices is vital, however, also challenging in practice. To address this issue, numerous researchers have proposed IMD key exchange protocols, in particular ones that leverage an Out-Of-Band (OOB) channel such as audio, vibration and physiological signals. These solutions have advantages over traditional key exchange, e.g.,...

In this paper, we extend Inner-Product Functional Encryption (IPFE), where there is just a vector in the key and a vector in the single sender's ciphertext, to two-client ciphertexts. More precisely, in our two-client functional encryption scheme, there are two data providers who can independently encrypt vectors $\mathbf{x}$ and $\mathbf{y}$ for a data consumer who can, from a functional decryption key associated to a vector $\mathbf{\alpha}$, compute $\sum \alpha_i x_i y_i = \mathbf{x}...

The transition from paper-based information to Electronic Health Records (EHRs) has driven various advancements in the modern healthcare industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, where the entire database is required to be...

Recently, a number of attacks have been demonstrated (like key reinstallation attack, called KRACK) on WPA2 protocol suite in Wi-Fi WLAN. As the firmware of the WLAN devices in the context of IoT, industrial systems, and medical devices is often not patched, detecting and preventing such attacks is challenging. In this paper, we design and implement a system, called CheckShake, to passively detect anomalies in the handshake of Wi-Fi security protocols, in particular WPA2, between a client...

There has been an intense concern for security alternatives because of the recent rise of cyber attacks, mainly targeting critical systems such as industry, medical, or energy ecosystem. Though the latest industry infrastructures largely depend on AI-driven maintenance, the prediction based on corrupted data undoubtedly results in loss of life and capital. Admittedly, an inadequate data-protection mechanism can readily challenge the security and reliability of the network. The shortcomings...

With the fast-growing technology and emerging innovations in the research arena, privacy and preservation of data predominantly in the medical field are highly essential. At the same time, there is a need for minimized storage of voluminous data in the medical repository. The inspiration for this research work to formulate the hybrid methodologies using improved Steganography, wavelet transform, and lossless compression for privacy and preservation of medical big data images and patient...

Implantable medical devices, sensors and wearables are widely deployed today. However, establishing a secure wireless communication channel to these devices is a major challenge, amongst others due to the constraints on energy consumption and the need to obtain immediate access in emergencies. To address this issue, researchers have proposed various key agreement protocols based on the measurement of physiological signals such as a person's heart signal. At the core of such protocols are...

Modern implantable cardiologic devices communicate via radio frequency techniques and nearby gateways to a backend server on the internet. Those implanted devices, gateways, and servers form an ecosystem of proprietary hardware and protocols that process sensitive medical data and is often vital for patients’ health. This paper analyzes the security of this Ecosystem, from technical gateway aspects, via the programmer, to configure the implanted device, up to the processing of personal...

Many interesting applications of pattern matching (e.g. deep-packet inspection or medical data analysis) target very sensitive data. In particular, spotting illegal behaviour in internet traffic conflicts with legitimate privacy requirements, which usually forces users (e.g. children, employees) to blindly trust an entity that fully decrypts their traffic in the name of security. The compromise between traffic analysis and privacy can be achieved through searchable encryption. However, as...

Clustering is an unsupervised machine learning technique that outputs clusters containing similar data items. In this work, we investigate privacy-preserving density-based clustering which is, for example, used in financial analytics and medical diagnosis. When (multiple) data owners collaborate or outsource the computation, privacy concerns arise. To address this problem, we design, implement, and evaluate the first practical and fully private density-based clustering scheme based on secure...

In recent years, smartphones have become an increasingly important storage facility for personal sensitive data ranging from photos and credentials up to financial and medical records like credit cards and person’s diseases. Trivially, it is critical to secure this information and only provide access to the genuine and authenticated user. Smartphone vendors have already taken exceptional care to protect user data by the means of various software and hardware security features like code...

Nowadays, genomic sequencing has become much more affordable for many people and, thus, many people own their genomic data in a digital format. Having paid for genomic sequencing, they want to make use of their data for different tasks that are possible only using genomics, and they share their data with third parties to achieve these tasks, e.g., to find their relatives in a genomic database. As a consequence, more genomic data get collected worldwide. The upside of the data collection is...

Sanitizable signatures (SaS) allow a (single) sanitizer, chosen by the signer, to modify and re-sign a message in a somewhat controlled way, that is, only editing parts (or blocks) of the message that are admissible for modification. This primitive is an efficient tool, with many formally defined security properties, such as unlinkability, transparency, immutability, invisibility, and unforgeability. An SaS scheme that satisfies these properties can be a great asset to the privacy of any...

Many organizations stand to benefit from pooling their data together in order to draw mutually beneficial insights -- e.g., for fraud detection across banks, better medical studies across hospitals, etc. However, such organizations are often prevented from sharing their data with each other by privacy concerns, regulatory hurdles, or business competition. We present Senate, a system that allows multiple parties to collaboratively run analytical SQL queries without revealing their individual...

Homomorphic encryption (HE) is a promising cryptographic primitive that enables computation over encrypted data, with a variety of applications including medical, genomic, and financial tasks. In Asiacrypt 2017, Cheon et al. proposed the CKKS scheme to efficiently support approximate computation over encrypted data of real numbers. HE schemes including CKKS, nevertheless, still suffer from slow encryption speed and large ciphertext expansion compared to symmetric cryptography. In this...

We apply multiparty computation (MPC) techniques to show, given a database that is secret-shared among multiple mutually distrustful parties, how the parties may obliviously construct a decision tree based on the secret data. We consider data with continuous attributes (i.e., coming from a large domain), and develop a secure version of a learning algorithm similar to the C4.5 or CART algorithms. Previous MPC-based work only focused on decision tree learning with discrete attributes (De Hoogh...

Conflict-free Replicated Data Types (CRDTs) are abstract data types that support developers when designing and reasoning about distributed systems with eventual consistency guarantees. In their core they solve the problem of how to deal with concurrent operations, in a way that is transparent for developers. However in the real world, distributed systems also suffer from other relevant problems, including security and privacy issues and especially when participants can be untrusted. In this...

In recent year’s security has become an important role in the field of Defense, Business, Medical and Industries.Different types of cryptography algorithms has been implemented in order to provide security with high performance. A hash function is a cryptography algorithm without a key such as MD5, SHA-family. Secure hash algorithm which is standardized by NIST as secured hashing in FIPS. In this paper we mainly focus on code optimization and increase the performance of SHA-512. To optimize...

In multi-client functional encryption (MC-FE) for predicate queries, clients generate ciphertexts of attributes $x_1, \ldots, x_n$ binding with a time period $T$ and store them on a cloud server, and the cloud server receives a token corresponding to a predicate $f$ from a trusted center and learns whether $f(x_1, \ldots, x_n) = 1$ or not by running the query algorithm on the multiple ciphertexts of the same time period. MC-FE for predicates can be used for a network event or medical data...

Privacy-preserving machine learning (PPML) has many applications, from medical image classification and anomaly detection to financial analysis. nGraph-HE enables data scientists to perform private inference of deep learning (DL) models trained using popular frameworks such as TensorFlow. nGraph-HE computes linear layers using the CKKS homomorphic encryption (HE) scheme. The non-polynomial activation functions, such as MaxPool and ReLU, are evaluated in the clear by the data owner who...

Pattern matching is one of the most fundamental and important paradigms in several application domains such as digital forensics, cyber threat intelligence, or genomic and medical data analysis. While it is a straightforward operation when performed on plaintext data, it becomes a challenging task when the privacy of both the analyzed data and the analysis patterns must be preserved. In this paper, we propose new provably correct, secure, and relatively efficient (compared to similar...

We present a range of novel attacks which exploit information about the volume of answers to range queries in encrypted database. Our attacks rely on a strategy which is simple yet robust and effective. We illustrate the robustness of our strategy in a number of ways. We show how i) to adapt the attack for several variations of a basic usage scenario ii) to defeat countermeasures intended to thwart the premise of our basic attack and iii) to perform partial reconstruction of secret data when...

Homomorphic Encryption is a breakthrough technology which can enable private cloud storage and computation solutions, and many applications have been described in the literature in the last few years. But before Homomorphic Encryption can be adopted in medical, health, and financial sectors to protect data and patient and consumer privacy, it will have to be standardized, most likely by multiple standardization bodies and government agencies. An important part of standardization is broad...

Security and safety-critical remote applications such as e-voting, online banking, industrial control systems and medical devices rely upon user interaction that is typically performed through web applications. Trusted path to such remote systems is critical in the presence of an attacker that controls the computer that the user operates. Such an attacker can observe and modify any IO data without being detected by the user or the server. We investigate the security of previous research...

Recently, in IEEE Transactions on Industrial Informatics, Fan et al. proposed a lightweight RFID protocol which has been suggested to be employed for protecting the Medical Privacy in an IoT system. However, the protocol has trivial flaws, as it is shown recently by Aghili et al., in Future Generation Computer Systems. Aghili et al. also proposed an improved version of the protocol, based on the similar designing paradigm, called SecLAP. Although the protocol's designers claimed full...

Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send back the results. One standing challenge in this setting is to ensure the privacy of the clients' sensitive data. Oblivious inference is the task of running the neural network on the client's input without disclosing the input or the result to the server. This paper introduces XONN, a novel...

Background Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithms to be securely deployed in the cloud because operations on encrypted genomic databases are conducted without revealing any individual genomes. Methods for secure computation have shown significant performance improvements over the last...

Machine learning on encrypted data is a cryptographic method for analyzing private and/or sensitive data while keeping privacy. In the training phase, it takes as input an encrypted training data and outputs an encrypted model without using the decryption key. In the prediction phase, it uses the encrypted model to predict results on new encrypted data. In each phase, no decryption key is needed, and thus the privacy of data is guaranteed while the underlying encryption is secure. It has...

Independent Component Analysis (ICA) is a powerful technique for blind source separation. It has been successfully applied to signal processing problems, such as feature extraction and noise reduction, in many different areas including medical signal processing and telecommunication. In this work, we propose a framework to apply ICA to denoise side-channel measurements and hence to reduce the complexity of key recovery attacks. Based on several case studies, we afterwards demonstrate the...

Human dignity demands that personal information, like medical and forensic data, be hidden from the public. But veils of secrecy designed to preserve privacy may also be abused to cover up lies and deceit by parties entrusted with Data, unjustly harming citizens and eroding trust in central institutions. Zero knowledge (ZK) proof systems are an ingenious cryptographic solution to the tension between the ideals of personal privacy and institutional integrity, enforcing the latter in a way...

To protect database confidentiality even in the face of full compromise while supporting standard functionality, recent academic proposals and commercial products rely on a mix of encryption schemes. The common recommendation is to apply strong, semantically secure encryption to the “sensitive” columns and protect other columns with property-revealing encryption (PRE) that supports operations such as sorting. We design, implement, and evaluate a new methodology for inferring data stored in...

The secure storage of long-lived sensitive data is constantly growing in its relevance due to the ever increasing digitization of documents. One very important challenge of this research field is to provide confidentiality for the stored data even in the long term. The only known approach to achieve this, as required, for instance, for medical records, is to use proactive secret sharing. However, all currently known schemes suffer from being inefficient. They require information-theoretic...

We analyse the security of database encryption schemes supporting range queries against persistent adversaries. The bulk of our work applies to a generic setting, where the adversary's view is limited to the set of records matched by each query (known as access pattern leakage). We also consider a more specific setting where certain rank information is also leaked. The latter is inherent to multiple recent encryption schemes supporting range queries, including Kerschbaum's FH-OPE scheme (CCS...

In this work the first linearly homomorphic authenticated encryption scheme with public verifiability and provable correctness, called LEPCoV, is presented. It improves the initial proposal by avoiding false negatives during the verification algorithm. This work provides a detailed description of LEPCoV, a comparison with the original scheme, a security and correctness proof, and a performance analysis showing that all algorithms run in reasonable time for parameters that are...

We describe novel, adaptive user interface attacks, where the adversary attaches a small device to the interface that connects user input peripherals to the target system. The device executes the attack when the authorized user is performing safety-, or security-critical operations, by modifying or blocking user input, or injecting new events. Although the adversary fully controls the user input channel, to succeed he needs to overcome a number of challenges, including the inability to...

With the synchronous development of both cloud computing and machine learning techniques, the clients are preferring to resort to the cloud server with substantial resources to train learning model. However, in this outsourcing paradigm it is of vital significance to address the privacy concern of client's data. Many researchers have been focusing on preserving the privacy of client's data in learning model. Recently, Wang et al. presented a privacy-preserving single-layer perceptron...

Cryptography is essential for the security of Internet communication, cars, and implanted medical devices. However, many commonly used cryptosystems will be completely broken once big quantum computers exist. Post-quantum cryptography is cryptography under the assumption that the attacker has a large quantum computer; post-quantum cryptosystems strive to remain secure even in this scenario. This relatively young research area has seen some successes in identifying mathematical operations...

Pinocchio is a practical zk-SNARK that allows a prover to perform cryptographically verifiable computations with verification effort sometimes less than performing the computation itself. A recent proposal showed how to make Pinocchio adaptive (or ``hash-and-prove''), i.e., to enable proofs with respect to computation-independent commitments. This enables computations to be chosen after the commitments have been produced, and for data to be shared in different computations in a flexible way....

Apart from their numerous other benefits, online social networks (OSNs) allow users to jointly compute on each other’s data (e.g., profiles, geo-locations, medical records, etc.). Privacy issues naturally arise in this setting due to the sensitive nature of the exchanged information. Ideally, nothing about a user’s data should be revealed to the OSN provider or "non-friend" users, and even her "friends" should only learn the output of a joint computation. In this work we propose the first...

Implantable Medical Devices (IMDs) are used to monitor and control patients with chronic diseases. A growing number of IMDs are equipped with a wireless interface that allows non-invasive monitoring and reprogramming through an external device, also known as device programmer. However, this wireless interface also brings important security and privacy risks that may lead to remote attacks. In this domain, the use of cryptography is challenging due to the inherent tensions between security vs...

When a group of individuals and organizations wish to compute a stable matching---for example, when medical students are matched to medical residency programs---they often outsource the computation to a trusted arbiter in order to preserve the privacy of participants' preferences. Secure multi-party computation offers the possibility of private matching processes that do not rely on any common trusted third party. However, stable matching algorithms have previously been considered...

Sharing the medical records of individuals among healthcare providers and researchers around the world can accelerate advances in medical research. While the idea seems increasingly practical due to cloud data services, maintaining patient privacy is of paramount importance. Standard encryption algorithms help protect sensitive data from outside attackers but they cannot be used to compute on this sensitive data while being encrypted. Homomorphic Encryption (HE) presents a very useful tool...

Polymorphic encryption and Pseudonymisation, abbreviated as PEP, form a novel approach for the management of sensitive personal data, especially in health care. Traditional encryption is rather rigid: once encrypted, only one key can be used to decrypt the data. This rigidity is becoming an every greater problem in the context of big data analytics, where different parties who wish to investigate part of an encrypted data set all need the one key for decryption. Polymorphic encryption is a...

This work addresses a basic problem of security systems that operate on very sensitive information, such as healthcare data. Specifically, we are interested in the problem of privately handling medical data represented by rational numbers. Considering the complicated computations on encrypted medical data, one of the natural and powerful tools for ensuring privacy of the data is fully homomorphic encryption (FHE). However, because the plaintext domain of known FHE schemes is restricted to a...

Electronic Health Record (EHR) system facilitates us a lot for health record management. Privacy risk of patients' records is the dominating obstacle in the widely deployed EHRs. Role-based access control (RBAC) schemes offer an access control on EHRs according to one's role. Only the medical staff with roles satisfying the specified access policies can read EHRs. In existing schemes, attackers can link patients' identities to their doctors. Therefore, the classification of patients'...

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive data such as private or medical information, cares are necessary. In this paper, we propose a secure system for protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our...

This report has two goals. First, we review guidelines for using the CryptDB system [PRZB11, Pop14] securely by the administrators of database applications. These guidelines were already described in [PRZB11] and elaborated on in [Pop14], but in light of some recent work [NKW15] that applied these guidelines incorrectly, a short document devoted to summarizing these guidelines may be useful. Second, we explain that the study of Naveed, Kamara, and Wright [NKW15] represents an unsafe usage...

Radio Frequency Identification (RFID) is a modern communication technology, which provides authentication and identification through a nonphysical contact. Recently, the use of this technology is almost developed in healthcare environments. Although RFID technology can prepare sagacity in systems, privacy and security issues ought to be considered before. Recently, in 2015, Li et al. proposed a hash-based RFID authentication protocol in medication verification for healthcare. In this paper,...

The energy cost of asymmetric cryptography is a vital component of modern secure communications, which inhibits its wide spread adoption within the ultra-low energy regimes such as Implantable Medical Devices (IMDs) and Radio Frequency Identification (RFID) tags. The ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic tool, where an encryptor can decide the access policy that who can decrypt the data. Thus, the data will be protected from the unauthorized...

Session key agreement protocol using smart card is extremely popular in client-server environment for secure communication. Remote user authentication protocol plays a crucial role in our daily life such as e-banking, bill-pay, online games, e-recharge, wireless sensor network, medical system, ubiquitous devices etc. Recently, Djellali et al. proposed a session key agreement protocol using smart card for ubiquitous devices. The main focus of this paper is to analyze security pitfalls of...

Computation based on genomic data is becoming increasingly popular today, be it for medical or other purposes such as ancestry or paternity testing. Non-medical uses of genomic data in a computation often take place in a server-mediated setting where the server offers the ability for joint genomic testing between the users. Undeniably, genomic data is highly sensitive, which in contrast to other biometry types, discloses a plethora of information not only about the data owner, but also about...

The availability of vast amounts of data is changing how we can make medical discoveries, predict global market trends, save energy, and develop new educational strategies. In certain settings such as Genome Wide Association Studies or deep learning, the sheer size of data (patient files or labeled examples) seems critical to making discoveries. When data is held distributedly by many parties, as often is the case, they must share it to reap its full benefits. One obstacle to this...

Machine learning classification is used in numerous settings nowadays, such as medical or genomics predictions, spam detection, face recognition, and financial predictions. Due to privacy concerns in some of these applications, it is important that the data and the classifier remain confidential. In this work, we construct three major classification protocols that satisfy this privacy constraint: hyperplane decision, Naïve Bayes, and decision trees. These protocols may also be combined...

Even as data and analytics driven applications are becoming increasingly popular, retrieving data from shared databases poses a threat to the privacy of their users. For example, investors/patients retrieving records about interested stocks/diseases from a stock/medical database leaks sensitive information to the database server. PIR (Private Information Retrieval) is a promising security primitive to protect the privacy of users' interests. PIR allows the retrieval of a data record from a...

Ciphertext-Policy Attribute-Based Proxy Re-Encryption (CP-ABPRE) extends the traditional Proxy Re-Encryption (PRE) by allowing a semi-trusted proxy to transform a ciphertext under an access policy to the one with the same plaintext under another access policy (i.e.attribute-based re-encryption). The proxy, however, learns nothing about the underlying plaintext. CP-ABPRE has many real world applications, such as fine-grained access control in cloud storage systems and medical records sharing...

Embedded computing devices (such as actuators, controllers and sensors of various sizes) increasingly permeate many aspects of modern life: from medical to automotive, from building and factory automation to weapons, from critical infrastructures to home entertainment. Despite their specialized nature as well as limited resources and connectivity, these devices are now becoming increasingly popular and attractive targets for various attacks, especially, remote malware infestations. There has...

Recently, Radio Frequency Identification (RFID) technique has been widely deployed in many applications, such as medical drugs management in hospitals and missing children searching in amusement parks. The applications basically can be classified into two types: non-public key cryptosystem (PKC)-based and PKC-based. However, many of them have been found to be flawed in the aspect of privacy problem. Therefore, many researchers tried to resolve this problem. They mainly investigated on how...

The prospect of outsourcing an increasing amount of data storage and management to cloud services raises many new privacy concerns for individuals and businesses alike. The privacy concerns can be satisfactorily addressed if users encrypt the data they send to the cloud. If the encryption scheme is homomorphic, the cloud can still perform meaningful computations on the data, even though it is encrypted. In fact, we now know a number of constructions of fully homomorphic encryption schemes...

We provide a design and implementation of self-protecting electronic medical records (EMRs) using attribute-based encryption. Our system allows healthcare organizations to export EMRs to storage locations outside of their trust boundary, including mobile devices, Regional Health Information Organizations (RHIOs), and cloud systems such as Google Health. In contrast to some previous approaches to this problem, our solution is designed to maintain EMR availability even when providers are...

Sanitizable signatures provide several security features which are useful in many scenarios including military and medical applications. Sanitizable signatures allow a semi-trusted party to update some part of the digitally signed document without interacting with the original signer. Such schemes, where the verifer cannot identify whether the message has been sanitized, are said to possess strong transparency. In this paper, we have described the first efficient and provably secure...

General two-party Secure Function Evaluation (SFE) allows mutually distrusting parties to (jointly) correctly compute \emph{any} function on their private input data, without revealing the inputs. SFE, properly designed, guarantees to satisfy the most stringent security requirements, even for interactive computation. Two-party SFE can benefit almost any client-server interaction where privacy is required, such as privacy-preserving credit checking, medical classification, or face...

Diagnostic and classification algorithms play an important role in data analysis, with applications in areas such as health care, fault diagnostics, or benchmarking. Branching programs (BP) is a popular representation model for describing the underlying classification/diagnostics algorithms. Typical application scenarios involve a client who provides data and a service provider (server) whose diagnostic program is run on client's data. Both parties need to keep their inputs private. We...

In an oblivious transfer (OT) protocol, a Sender with messages M_1,...,M_N and a Receiver with indices s_1,...,s_k interact in such a way that at the end the Receiver obtains M_{s_1},...,M_{s_k} without learning anything about the other messages, and the Sender does not learn anything about s_1,...,s_k. In an adaptive protocol, the Receiver may obtain M_{s_{i-1}} before deciding on $s_i$. Efficient adaptive OT protocols are interesting both as a building block for secure multiparty...

Designing secure protocols in the Universal Composability (UC) framework confers many advantages. In particular, it allows the protocols to be securely used as building blocks in more complex protocols, and assists in understanding their security properties. Unfortunately, most existing models in which universally composable computation is possible (for useful functionalities) require a trusted setup stage. Recently, Katz [Eurocrypt '07] proposed an alternative to the trusted setup...