Process Sensitive Data Research Articles

Isolation of corporate local area networks using server virtualization

Secure enterprises have Local Area Networks (LANs) that store and process sensitive data that should not be accessed from outside. At the same time, no modern enterprise can successfully function without a LAN which provides the Internet access for the computers of the sales department, technical support service and E-mail server. In order to ensure information security, these two network segments are usually physically isolated from each other by means of exception of any cable interconnections. Enterprises with high demands for server uptime use server virtualization, which requires connecting physical servers to disk storage of virtual server image files. When highly reliable servers are needed only in one of two independent corporate network segments, the cost of creating such a server cluster is reasonable and fast paying off. But when a company encounters the need to place a small but highly reliable server into another network segment, the following problem arises: only server cluster with expensive components can provide the required reliability, but to solve the tasks assigned to the new server, the cluster computing capacity would be too high, and deploying of a cluster would be extremely costly and economically unjustified. On the other hand, the required computing capacity for a new server would be taken from the existing server cluster by creating a new virtual server without any expenses. However, in such case it is necessary to ensure isolation of the new virtual server from the network segment the physical hosts servers are connected to. The article examines the methods of configuring the LAN that allow to isolate virtual and physical servers at the network level. The authors also consider the episode when it is necessary to transfer files between the two computers located in the LAN segments isolated from each other and gives the appropriate solution.

Hyperspectral camera application for remote gas analysis

The report reviewed current systems for monitoring gas leaks at industrial facilities. Since in industrial plants the emission of some gases can be technologically justified, and the presence of others indicates a violation of the technological process and is a sign of an emergency situation, a possible option is described not only for detection the fact of leakage, but also for the classification of detected gases. We considered existing hardware-software complexes including acousto-optical, and listed their shortcomings, such as insufficient sensitivity and low data processing speed. In general, we describe the novel technical implementation of imaging spectrometry. For simultaneous detection of spatial and spectral properties of objects, the infrared sensor is divided into a mosaic of the smallest optical filters. Each filter passes individual intervals of the spectrum, blocking the others. The uncooled detector 640×512 can be covered with 16 filters, which spectral characteristics should correlate with the absorption spectra of target gases.

Integrating GDPR in ISO 15189 for Medical Laboratories: Major Aspects and Perspectives.

Medical laboratories process and store sensitive data during four major phases: arrival of patients in the laboratory premises and registration of their data, pre-analytical, analytical and post-analytical phases. ISO 15189 has specific requirements concerning the management of the laboratory data in terms of security, availability and protection. The aim of the present study was to examine major aspects of the General Data Protection Regulation (GDPR) integration in medical laboratories that comply with the ISO 15189 standard, including data breach and informed consent. To the best of our knowledge, this is the first study dealing with this subject in the healthcare sector. Accredited medical laboratories need to modify their ISO 15189 Quality System documentation and processes applying appropriate additions and adjustments in order to incorporate GDPR requirements in a clear manner.

SoftME: A Software-Based Memory Protection Approach for TEE System to Resist Physical Attacks

The development of the Internet of Things has made embedded devices widely used. Embedded devices are often used to process sensitive data, making them the target of attackers. ARM TrustZone technology is used to protect embedded device data from compromised operating systems and applications. But as the value of the data stored in embedded devices increases, more and more effective physical attacks have emerged. However, TrustZone cannot resist physical attacks. We propose SoftME, an approach that utilizes the on-chip memory space to provide a trusted execution environment for sensitive applications. We protect the confidentiality and integrity of the data stored on the off-chip memory. In addition, we design task scheduling in the encryption process. We implement a prototype system of our approach on the development board supporting TrustZone and evaluate the overhead of our approach. The experimental results show that our approach improves the security of the system, and there is no significant increase in system overhead.

Dados pessoais sensíveis e a tutela de direitos fundamentais: uma análise à luz da lei geral de proteção de dados (Lei 13.709/18)

O presente artigo pretende aproximar o conceito de dados pessoais sensíveis de uma teoria de direitos fundamentais, por meio de uma interpretação dos princípios e valores constitucionais que justificam a proteção do direito à privacidade. Pretende-se investigar a Lei Geral de Proteção de Dados - Lei 13.709/2018 - e as políticas por ela assumidas no que diz respeito ao trata­mento de dados pessoais sensíveis tanto pelo Estado, quanto pelo Mercado. Sustenta-se a necessidade de um tratamento restrito dos dados pessoais sensíveis como forma de proteção contra o seu uso discriminatório, visando a promoção plena do exercício democrático.

Secure data processing for IoT middleware systems

Increasingly, more manufacturing companies are equipping their products with smart capabilities which allow them to provide more informed services to customers. Unfortunately, most of these companies lack enough technical capabilities to build scalable platforms to process data collected by the deployed devices. As a result, these device manufacturers rely on IoT middleware companies to provide the needed processing capabilities and scalability. With the proliferation of these middleware services in handling data and the increase in the risk of data leakage and data breaches, we propose an approach that ensures data protection by leveraging trusted hardware-based technology from the recent Software Guard Extension (SGX) provided by Intel. SGX is a new technology that enforces strong isolation by running a process in a secure sandbox called enclave, and it offers remote attestation to ensure computations on an untrusted system are running within an enclave. By deploying SGX in the IoT gateway and the cloud service, we show that our approach prevents attacks on IoT data in transit as well as at rest by using key hashing to enforce message integrity. Our proposed framework ensures the protection of user data on third-party IoT middleware platforms by dividing the IoT data platform into trusted and untrusted modules and ensures the execution of all sensitive data processing in the trusted module which runs inside a hardware protected memory region called as enclave. Our approach enables the user to implement data access policy control within the enclave. Our proposed framework allows the user to verify that the application is running in an authenticated SGX machine and to ensure the application is not modified by a platform owner as a result of the remote attestation mechanism provided by SGX. Meanwhile, our approach defeats low-level attacks and keeps all data securely encrypted without introducing significant overhead.

SIFR annotator: ontology-based semantic annotation of French biomedical text and clinical notes

BackgroundDespite a wide adoption of English in science, a significant amount of biomedical data are produced in other languages, such as French. Yet a majority of natural language processing or semantic tools as well as domain terminologies or ontologies are only available in English, and cannot be readily applied to other languages, due to fundamental linguistic differences. However, semantic resources are required to design semantic indexes and transform biomedical (text)data into knowledge for better information mining and retrieval.ResultsWe present the SIFR Annotator (http://bioportal.lirmm.fr/annotator), a publicly accessible ontology-based annotation web service to process biomedical text data in French. The service, developed during the Semantic Indexing of French Biomedical Data Resources (2013–2019) project is included in the SIFR BioPortal, an open platform to host French biomedical ontologies and terminologies based on the technology developed by the US National Center for Biomedical Ontology. The portal facilitates use and fostering of ontologies by offering a set of services –search, mappings, metadata, versioning, visualization, recommendation– including for annotation purposes. We introduce the adaptations and improvements made in applying the technology to French as well as a number of language independent additional features –implemented by means of a proxy architecture– in particular annotation scoring and clinical context detection. We evaluate the performance of the SIFR Annotator on different biomedical data, using available French corpora –Quaero (titles from French MEDLINE abstracts and EMEA drug labels) and CépiDC (ICD-10 coding of death certificates)– and discuss our results with respect to the CLEF eHealth information extraction tasks.ConclusionsWe show the web service performs comparably to other knowledge-based annotation approaches in recognizing entities in biomedical text and reach state-of-the-art levels in clinical context detection (negation, experiencer, temporality). Additionally, the SIFR Annotator is the first openly web accessible tool to annotate and contextualize French biomedical text with ontology concepts leveraging a dictionary currently made of 28 terminologies and ontologies and 333 K concepts. The code is openly available, and we also provide a Docker packaging for easy local deployment to process sensitive (e.g., clinical) data in-house (https://github.com/sifrproject).

Trusted Cameras on Mobile Devices Based on SRAM Physically Unclonable Functions.

Nowadays, there is an increasing number of cameras placed on mobile devices connected to the Internet. Since these cameras acquire and process sensitive and vulnerable data in applications such as surveillance or monitoring, security is essential to avoid cyberattacks. However, cameras on mobile devices have constraints in size, computation and power consumption, so that lightweight security techniques should be considered. Camera identification techniques guarantee the origin of the data. Among the camera identification techniques, Physically Unclonable Functions (PUFs) allow generating unique, distinctive and unpredictable identifiers from the hardware of a device. PUFs are also very suitable to obfuscate secret keys (by binding them to the hardware of the device) and generate random sequences (employed as nonces). In this work, we propose a trusted camera based on PUFs and standard cryptographic algorithms. In addition, a protocol is proposed to protect the communication with the trusted camera, which satisfies authentication, confidentiality, integrity and freshness in the data communication. This is particularly interesting to carry out camera control actions and firmware updates. PUFs from Static Random Access Memories (SRAMs) are selected because cameras typically include SRAMs in its hardware. Therefore, additional hardware is not required and security techniques can be implemented at low cost. Experimental results are shown to prove how the proposed solution can be implemented with the SRAM of commercial Bluetooth Low Energy (BLE) chips included in the communication module of the camera. A proof of concept shows that the proposed solution can be implemented in low-cost cameras.

Comparison of Cost of Protection against Differential Power Analysis of Selected Authenticated Ciphers

Authenticated ciphers, which combine the cryptographic services of confidentiality, integrity, and authentication into one algorithmic construct, can potentially provide improved security and efficiencies in the processing of sensitive data. However, they are vulnerable to side-channel attacks such as differential power analysis (DPA). Although the Test Vector Leakage Assessment (TVLA) methodology has been used to confirm improved resistance of block ciphers to DPA after application of countermeasures, extension of TVLA to authenticated ciphers is non-trivial, since authenticated ciphers have expanded input and output requirements, complex interfaces, and long test vectors which include protocol necessary to describe authenticated cipher operations. In this research, we upgrade the FOBOS test architecture with capability to perform TVLA on authenticated ciphers. We show that FPGA implementations of the CAESAR Round 3 candidates ACORN, Ascon, CLOC (with AES and TWINE primitives), SILC (with AES, PRESENT, and LED primitives), JAMBU (with AES and SIMON primitives), and Ketje Jr.; as well as AES-GCM, are vulnerable to 1st order DPA. We then use threshold implementations to protect the above cipher implementations against 1st order DPA, and verify the effectiveness of countermeasures using the TVLA methodology. Finally, we compare the unprotected and protected cipher implementations in terms of area, performance (maximum frequency and throughput), throughput-to-area (TP/A) ratio, power, and energy per bit (E/bit). Our results show that ACORN consumes the lowest number of resources, has the highest TP/A ratio, and is the most energy-efficient of all DPA-resistant implementations. However, Ketje Jr. has the highest throughput.

ПОСТРОЕНИЕ ЗАЩИЩЕННОЙ СИСТЕМЫ УПРАВЛЕНИЯ МНОГОМЕРНЫМИ СТРУКТУРАМИ ДАННЫХ

Nowadays one should not underestimate importance of informational technologies across all fields of humans’ life. Computational technologies and tools became crucial parts of any human activity. However, since information is becoming a strategic resource, it is necessary to ensure its security by all means. A large number of contemporary studies have been carried on in the field of informational security. Recently, multidimensional information technologies have become popular for data analysis and processing. This has led to a contradiction between ever growing demand of multidimensional data processing systems and inability to properly secure such system due to insufficient research within this field. As a result, sensitive multidimensional data cannot be securely processed due to lack of proper secure systems able to ensure appropriate level of informational security. Authors decided to develop such system. The paper proposes architecture of multidimensional data management system capable of secure information management built up as a multidimensional hypercube. Proposed architecture consists of a description of main system components, a description of task performed by those components and principles of their communication. The paper describes a way to build a system which ensures safe read/write operations for confidential information organized as multidimensional hypercube. The proposed solution allows secure processing of sensitive multidimensional data and therefore extending the area where such systems can be used.

ConTPL: Controlling Temporal Privacy Leakage in Differentially Private Continuous Data Release.

In many real-world systems, such as Internet of Thing, sensitive data streams are collected and analyzed continually. To protect privacy, a number of mechanisms are designed to achieve ϵ-differential privacy for processing sensitive streaming data, whose privacy loss is rigorously controlled within a given parameter ϵ. However, most of the existing studies do not consider the effect of temporal correlations among the continuously generated data on the privacy loss. Our recent work reveals that, the privacy loss of a traditional DP mechanism (e.g., Laplace mechanism) may not be bounded by ϵ due to temporal correlations. We call such unexpected privacy loss Temporal Privacy Leakage (TPL). In this demonstration, we design a system, ConTPL, which is able to automatically convert an existing differentially private streaming data release mechanism into one bounding TPL within a specified level. ConTPL also provides an interactive interface and real-time visualization to help data curator understand and explore the effect of different parameters on TPL.

Why the ‘Computer says no’: illustrating big data’s discrimination risk through complex systems science

The GDPR poses special requirements for the processing of sensitive data, but it is not clear whether these requirements are sufficient to prevent the risk associated with this processing because this risk is not clearly defined. Furthermore, the GDPR’s clauses on the processing of—and profiling based on—sensitive data do not sufficiently account for the fact that individual data subjects are parts of complex systems, whose emergent properties betray sensitive traits from non-sensitive data. The algorithms used to process big data are largely opaque to both controllers and data subjects: if the output of an algorithm has discriminatory effects coinciding with sensitive traits because the algorithm accidentally discerns an emergent property, this may remain unnoticed. At the moment, there are no remedies that can prevent the discovery of sensitive traits from non-sensitive data. Managing the risks resulting from processing data that can reveal sensitive traits requires a strategy combining precautionary measures, public discourse, and enforcement until the risks are more completely understood. Insights from complex systems science are likely to be useful in better understanding these risks.

Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator.

SummarySecond use of clinical data commonly involves annotating biomedical text with terminologies and ontologies. The National Center for Biomedical Ontology Annotator is a frequently used annotation service, originally designed for biomedical data, but not very suitable for clinical text annotation. In order to add new functionalities to the NCBO Annotator without hosting or modifying the original Web service, we have designed a proxy architecture that enables seamless extensions by pre-processing of the input text and parameters, and post processing of the annotations. We have then implemented enhanced functionalities for annotating and indexing free text such as: scoring, detection of context (negation, experiencer, temporality), new output formats and coarse-grained concept recognition (with UMLS Semantic Groups). In this paper, we present the NCBO Annotator+, a Web service which incorporates these new functionalities as well as a small set of evaluation results for concept recognition and clinical context detection on two standard evaluation tasks (Clef eHealth 2017, SemEval 2014).Availability and implementationThe Annotator+ has been successfully integrated into the SIFR BioPortal platform—an implementation of NCBO BioPortal for French biomedical terminologies and ontologies—to annotate English text. A Web user interface is available for testing and ontology selection (http://bioportal.lirmm.fr/ncbo_annotatorplus); however the Annotator+ is meant to be used through the Web service application programming interface (http://services.bioportal.lirmm.fr/ncbo_annotatorplus). The code is openly available, and we also provide a Docker packaging to enable easy local deployment to process sensitive (e.g. clinical) data in-house (https://github.com/sifrproject).Supplementary information Supplementary data are available at Bioinformatics online.

A Component-Based Approach for Securing Indoor Home Care Applications.

eHealth systems have adopted recent advances on sensing technologies together with advances in information and communication technologies (ICT) in order to provide people-centered services that improve the quality of life of an increasingly elderly population. As these eHealth services are founded on the acquisition and processing of sensitive data (e.g., personal details, diagnosis, treatments and medical history), any security threat would damage the public’s confidence in them. This paper proposes a solution for the design and runtime management of indoor eHealth applications with security requirements. The proposal allows applications definition customized to patient particularities, including the early detection of health deterioration and suitable reaction (events) as well as security needs. At runtime, security support is twofold. A secured component-based platform supervises applications execution and provides events management, whilst the security of the communications among application components is also guaranteed. Additionally, the proposed event management scheme adopts the fog computing paradigm to enable local event related data storage and processing, thus saving communication bandwidth when communicating with the cloud. As a proof of concept, this proposal has been validated through the monitoring of the health status in diabetic patients at a nursing home.

User-friendly deniable storage for mobile devices

Mobile devices are prevalently used to process sensitive data, but traditional encryption may not work when an adversary is able to coerce the device owners to disclose the encryption keys. Plausibly Deniable Encryption (PDE) is thus designed to protect sensitive data against this powerful adversary. In this paper, we present MobiPluto, a user-friendly PDE scheme for denying the existence of sensitive data stored on mobile devices. A salient difference between MobiPluto and the existing PDE systems is that any block-based file systems can be deployed on top of it. To further improve usability and deniability of MobiPluto, we introduce a fast switching mechanism and incorporate the widely-used Near Field Communication (NFC) technology. Users can securely switch from the public mode to the hidden mode within 10 seconds, which is a significant improvement compared to previous solutions. Users can also store strong passwords on NFC cards and tap them to enter the system, which significantly liberates them from the burden of memorizing and typing strong passwords. Most importantly, the users can deny the existence of the hidden data without the skill to camouflage as long as the NFC cards are used properly.

Carving database storage to detect and trace security breaches

Database Management Systems (DBMS) are routinely used to store and process sensitive enterprise data. However, it is not possible to secure data by relying on the access control and security mechanisms (e.g., audit logs) of such systems alone – users may abuse their privileges (no matter whether granted or gained illegally) or circumvent security mechanisms to maliciously alter and access data. Thus, in addition to taking preventive measures, the major goal of database security is to 1) detect breaches and 2) to gather evidence about attacks for devising counter measures. We present an approach that evaluates the integrity of a live database, identifying and reporting evidence for log tampering. Our approach is based on forensic analysis of database storage and detection of inconsistencies between database logs and physical storage state (disk and RAM). We apply our approach to multiple DBMS to demonstrate its effectiveness in discovering malicious operations and providing detailed information about the data that was illegally accessed/modified.

Supervised machine learning using encrypted training data

Preservation of privacy in data mining and machine learning has emerged as an absolute prerequisite in many practical scenarios, especially when the processing of sensitive data is outsourced to an external third party. Currently, privacy preservation methods are mainly based on randomization and/or perturbation, secure multiparty computations and cryptographic methods. In this paper, we take advantage of the partial homomorphic property of some cryptosystems to train simple machine learning models with encrypted data. Our basic scenario has three parties: multiple Data Owners, which provide encrypted training examples; the Algorithm Owner (or Application), which processes them to adjust the parameters of its models; and a semi-trusted third party, which provides privacy and secure computation services to the Application in some operations not supported by the homomorphic cryptosystem. In particular, we focus on two issues: the use of multiple-key cryptosystems, and the impact of the quantization of real-valued input data required before encryption. In addition, we develop primitives based on the outsourcing of a reduced set of operations that allows to implement general machine learning algorithms using efficient dedicated hardware. As applications, we consider the training of classifiers using privacy-protected data and the tracking of a moving target using encrypted distance measurements.

E2FM: an encrypted and compressed full-text index for collections of genomic sequences.

Next Generation Sequencing (NGS) platforms and, more generally, high-throughput technologies are giving rise to an exponential growth in the size of nucleotide sequence databases. Moreover, many emerging applications of nucleotide datasets-as those related to personalized medicine-require the compliance with regulations about the storage and processing of sensitive data. We have designed and carefully engineered E 2 FM -index, a new full-text index in minute space which was optimized for compressing and encrypting nucleotide sequence collections in FASTA format and for performing fast pattern-search queries. E 2 FM -index allows to build self-indexes which occupy till to 1/20 of the storage required by the input FASTA file, thus permitting to save about 95% of storage when indexing collections of highly similar sequences; moreover, it can exactly search the built indexes for patterns in times ranging from few milliseconds to a few hundreds milliseconds, depending on pattern length. Source code is available at https://github.com/montecuollo/E2FM . ferdinando.montecuollo@unicampania.it. Supplementary data are available at Bioinformatics online.

Linking survey responses and administrative records without respondent consent to link; a discussion paper

ABSTRACTObjectives(i) Review the application and interpretation of the Data Protection Act (DPA) 1998; clarifying whether individual consent is required for data linkage for secondary research purposes, in consideration of the policies and principles of the UK Administrative Data Research Network (ADRN).
 (ii) Determine ethical, logistical or ‘tactical’ factors researchers might have to take into consideration.
 ApproachLinking survey data to administrative records offer potential advantage to both researchers and survey respondents. Informed, specific and explicit consent is typically a prerequisite for linkage. However, not all respondents consent to data-linkage resulting in a reduced and potentially biased sub-sample for analysis. In Northern Ireland consent rates for record linkage are typically about 50%.
 Discussion with the ICO confirms that the DPA may encourage rather than restrict research. S33 ‘research exemption’ supports secondary use of survey data subject to conditions such as that is for research purposes, it is not incompatible with the original purpose, and would not cause the data subjects substantial damage or distress. Other DPA principles remain in force; Principle 1 (fair and lawful processing) and the need to make data subjects ‘aware’ of the research: explicit consent is only one route by which this can be achieved. The Processing Sensitive Data Order (2000) protects the privacy of individuals. Research must be of substantial public interest with access only to data necessary to answer the research question. The ADRN enables access to de identified data for research purposes where identified public benefit is independently assured, maintains the privacy of individuals and ensures lawful “conditions of processing” are met.
 Resulting discussionLegal considerations aside, researchers may face other obstacles; the first is technical as the surveying agency may have deleted all linkable identifiers. The second is ethical as research ethics committee approval is a usual precondition. De-identification of individual subjects should be grounds for ethical approval where the research proposal includes a publication plan with appropriate methods to inform participants of research conducted and findings. Finally, the data custodians may not agree to the linkage for sound ‘tactical’ longer-term reasons, even if convinced of its legality.
 ConclusionUse of de identified survey data for research purposes is possible via the UK ADRN but raises other considerations for researchers and data custodians. We argue that this option should be used in limited circumstances.

A Modified Cloud-Based Cryptographic Agent for Cloud Data Integrity

In spite of all the advantages delivered by cloud computing, several challenges are hindering the migration of customer software and data into the cloud. On top of the list is the security and privacy concerns arising from the storage and processing of sensitive data on remote machines that are not owned, or even managed by the customers themselves. In this paper, initially a homomorphic encryption-based Cryptographic Agent is proposed. The proposed Cryptographic Agent is based on Paillier scheme, and is supported by user-configurable software protection and data privacy categorization agents, as well as set of accountable auditing services required to achieve legal compliance and certification. This scheme was tested using different text documents with different sizes. Testing results showed that as the size of the document increases, the size of the generated key increases dramatically causing a major problem in regards to the processing time and the file size especially for large documents. This leaded us to the second part of this research which is: a modified security architecture that adds two major autonomic security detective agents to the multi-agent architecture of cloud data storage. In this paper, we focus on the first agent namely (Automated Master Agent, AMA) that is added to the Multi Agent System Architecture (MASA) layer (cloud client-side) by which any changes happen in the document are mapped in a QR code encoded key print (KP). Experimental results after integrating these agents showed a 100% alternation detection accuracy and a superiority in extracting the KP of large and very large size documents which exceeds the currently available products and leverage the tamper-proof capabilities of cryptographic coprocessors to establish a secure execution domain in the computing cloud that is physically and logically protected from unauthorized access.