Privacy Protocol Research Articles

Nowhere to Hide: A Novel Private Protocol Identification Algorithm

In recent years, with the rapid development of mobile Internet and 5G technology, great changes have been brought to our lives, and human beings have stepped into the era of big data. These new features and techniques in 5G support many different types of mobile applications for users, which makes network security extremely challenging. Among them, more and more applications involve users’ private data, such as location information, financial information, and biological information. In order to prevent users’ privacy disclosure, most applications choose to use private protocols. However, such private protocols also provide a means for malware and malicious applications to steal users’ privacy and confidential data. From a more secure point of view, we need to provide a way for users to know how many private protocols are running on their mobile phones and distinguish which are authorized applications and which are not. Therefore, the analysis and identification of private protocols have become a hot topic in current research. How to extract the characteristics of network protocol effectively and identify the private protocol accurately becomes the most important part of this research. In this paper, we combine genetic algorithm and association rule algorithm and then propose a set of feature extraction algorithm and protocol recognition algorithm for unknown protocols. The experimental analysis based on the actual data shows that these methods can effectively solve the problems of feature extraction and recognition for unknown protocols and can greatly improve the accuracy of private protocol recognition.

An anonymity communication protocol for security and privacy of clients in IoT-based mobile health transactions

Mobile Health (mHealth) technology facilitates patients’ care and decreases healthcare costs by remotely monitoring healthcare processes and medical data management. Nonetheless, there are many concerns regarding the security, privacy, and anonymity of the mHealth transactions. Conventionally, privacy includes anonymity, untraceability, unlinkability, unforgeability, and confidentiality. Transactions’ confidentiality can be easily protected using confidential transaction techniques. However, anonymity, untraceability, unlinkability, and unforgeability properties are quite challenging issues in healthcare transactions. This paper proposes a UDP-based anonymous protocol for mHealth transactions to protect the security of data and privacy of clients’ identities. In this work, we aim to investigate utilizing one of the best anonymity solutions, onion structure (onion encryption, onion routing, and onion network), with blockchain smart contracts upon the UDP platform to make a protocol for transferring data in IoT-based mHealth applications. In the proposed protocol, every malicious client who sends false data can be easily detected using an identity disclosure process. In this way, mHealth clients, without exchanging sensitive identities, can establish an authorized connection and be protected against different kinds of attacks. According to our experimental results and security/privacy proofs, the proposed protocol has an acceptable computational cost and security protections for IoT-based mHealth transactions.

Development and validation of a drug adherence index for COPD.

BACKGROUND: Inhaled medications are the mainstay of treatment for chronic obstructive pulmonary disease (COPD). Despite their importance, adherence to these medications is low. Low adherence is linked to increased exacerbation rates, mortality rates, health care utilization, and, ultimately, increased costs. A drug adherence index (DAI) is a predictive modeling tool that identifies patients most likely to change adherence status so that they can be targeted for support programs. Optum has previously developed DAI tools for diabetes, hypertension, and high cholesterol. In this study, a COPD-specific DAI was developed. This DAI tool could be used to better target medication adherence support in patients with COPD, aiming to increase adherence. OBJECTIVES: To develop a COPD-specific DAI using (a) enrollment, medical, and pharmacy variables and (b) only enrollment and pharmacy variables for potential application to pharmacy benefit managers and pharmacy plans. METHODS: This was a retrospective observational study using health care claims among Medicare Advantage with Part D beneficiaries with COPD in the United States. Potential predictors of adherence were measured during a 1-year baseline period. The adherence outcome was measured during a subsequent 1-year at-risk period. Adherence to long-acting bronchodilators was defined as a proportion of days covered (PDC) ≥80%. Nonadherence was defined as a PDC of <80%. Patients were stratified according to their adherence status at baseline, and logistic regression models were developed separately for each set of patients. Separate models were also developed using enrollment, medical, and pharmacy variables (primary objective) or using enrollment and pharmacy variables only (secondary objective). RESULTS: A total of 61,507 patients met all inclusion and exclusion criteria. For the primary objective, at baseline, 31,142 patients were adherent and 30,365 patients were nonadherent. The final DAI model used to predict future nonadherence included 30 covariates, with 7 predictors from medical claims. The validated model c-statistic was 0.752. The final DAI model used to predict future adherence included 29 covariates; only 4 predictors were from medical claims. The validated model c-statistic was 0.691. Findings were similar for the secondary objective using only enrollment and pharmacy variables. CONCLUSIONS: This DAI was developed and validated specifically to predict future adherence status to long-acting bronchodilator medications among patients with COPD. The DAI models performed better for predicting nonadherence than predicting adherence. Both organizations with medical and pharmacy data and organizations with only pharmacy data could utilize the DAI tool to target patients for adherence programs, as results were similar with and without the use of medical variables. DISCLOSURES: This study was sponsored and funded by GlaxoSmithKline (HO-16-17938). The study sponsor participated in the conception and design of the study, analysis and interpretation of the data, and drafting and critical revision of the report and approved submission of the manuscript. All authors had access to the results of the analyses, reviewed and edited the manuscript, approved the final draft, and were involved in the decision to submit the manuscript for publication. The data contained in the Optum database contain proprietary elements owned by Optum and, therefore, cannot be broadly disclosed or made publicly available at this time. The disclosure of these data to third parties assumes certain data security and privacy protocols are in place and that the third party has executed a license agreement that includes restrictive agreements governing the use of the data. Bengtson, Buikema, and Bankcroft are employees at Optum, and Schilling is a former employee of Optum; their employment was not contingent on this work. Optum was funded by GlaxoSmithKline to conduct the study. Stanford was an employee of GlaxoSmithKline at the time of this study and holds stock in GlaxoSmithKline.

Assessing gage: an online tool for improving gender visibility in STEMM

Women continue to be underrepresented and less visible in the fields of science, technology, engineering, mathematics, and medicine (STEMM). 500 Women Scientists created and launched in January 2018 a global (&gt;140 countries to date), online, open-access directory of women in STEMM fields. This directory—recently renamed gage—now also includes gender diverse persons (i.e., additional underrepresented genders) in STEMM fields. The purpose of the directory is to make these scientists’ expertise easier to locate and access for conference organizers, journalists, policy makers, educators, and others. Here, we undertake an assessment of the directory using surveys, Google Analytics, and focus groups to understand its efficacy and direction to date and identify future improvements we pledge to undertake. Through this assessment—conducted externally and in accordance with privacy protocols by Concolor Research—we identified who and how people are using our directory, why people signed up to be a resource, and areas for improvement. Through such assessment, we can learn how to enhance the directory’s efficacy and our broader efforts to boost the visibility of underrepresented people in STEMM.

An Encrypted Field Locating Algorithm for Private Protocol Data Based on Data Reconstruction and Moment Eigenvector

Encrypted network data classification has received considerable attention in the industry and research communities for a long time. However, the emergence of new private applications and encryption protocols has brought more new challenges. The primary task of classification is to determine whether the sample is encrypted. When the specification of private protocol is unpublished, only the whole sample can be processed, and thus the unencrypted field which coexists with the encrypted field will seriously lower the classification effect. To tackle this problem, an algorithm based on data reconstruction and moment eigenvector is proposed, which can not only estimate the encryption result but also locate the encrypted field in each data sample. In the algorithm, the encryption probability sequence is calculated firstly by data reconstruction and CNN (Convolutional Neural Network) model transfer. Then, based on the derivative of the encryption probability sequence, the suspected encrypted field set is generated. Finally, encrypted field matching is performed based on the similarity of the four-dimensional moment eigenvector, and thus the possible encrypted field of each sample is obtained. The proposed algorithm achieved a recall rate of 93% and a precision rate of 72% in an experiment of distinguishing the encrypted/unencrypted ones of complex data. The forward coverage, reverse coverage, and F1 value in identifying encrypted field reached 89%, 90%, and 90%, respectively. Compared with the encrypted field matching methods based on the K-Nearest Neighbor algorithm, Dynamic Time Warping, Runs test, and Frequency test, the method proposed in this paper exhibited salient advantages.

Lower and Upper Bounds on the Randomness Complexity of Private Computations of AND

We consider multi-party information-theoretic private protocols, and specifically their randomness complexity. The randomness complexity of private protocols is of interest both because random bits are considered a scarce resource, and because of the relation between that complexity measure and other complexity measures of boolean functions such as the circuit size or the sensitivity of the function being computed [12, 17].

An Efficient Lightweight Authentication and Key Agreement Protocol for Patient Privacy

Tele-medical information system provides an efficient and convenient way to connect patients at home with medical personnel in clinical centers. In this system, service providers consider user authentication as a critical req... | Find, read and cite all the research you need on Tech Science Press

A new form of initialization vectors in the FMS attack of RC4 in WEP

Abstract S. Fluhrer, I. Mantin and A. Shamir (FMS) had put forward a fascinating attack of the key of RC4 in Wired Equivalent Privacy (WEP) protocol on the basis of special initialization vectors (IVs) of pattern (3, 255, v). Inspired by the FMS attack, this paper tries to find new pattern of IVs that can be used for recovering the key of RC4 in WEP. We discovered that IVs of new pattern (v, 257-v, 255) can also be used in the FMS attack in a very similar way. Combining the new pattern with the previous pattern (3, 255, v) could result in a higher proportion of IVs that can be exploited in the FMS attack.

Performance Evaluation of SNMPv1/2c/3 using Different Security Models on Raspberry Pi

The Simple Network Management Protocol (SNMP) is one of the dominant protocols for network monitoring and configuration. The first two versions of SNMP (v1 and v2c) use the Community-based Security Model (CSM), where the community is transferred in clear text, resulting in a low level of security. With the release of SNMPv3, the User-based Security Model (USM) and Transport Security Model (TSM) were proposed, with strong authentication and privacy at different levels. The Raspberry Pi family of Single-Board Computers (SBCs) is widely used for many applications. To help their integration into network management systems, it is essential to study the impact of the different versions and security models of SNMP on these SBCs. In this work, we carried out a performance analysis of SNMP agents running in three different Raspberry Pis (Pi Zero W, Pi 3 Model B, and Pi 3 Model B+). Our comparisons are based on the response time, defined as the time required to complete a request/response exchange between a manager and an agent. Since we did not find an adequate tool for our assessments, we developed our own benchmarking tool. We did numerous experiments, varying different parameters such as the type of requests, the number of objects involved per request, the security levels of SNMPv3/USM, the authentication and privacy protocols of SNMPv3/USM, the transport protocols, and the versions and security models of SNMP. Our experiments were executed with Net-SNMP, an open-source and comprehensive distribution of SNMP. Our tests indicate that SNMPv1 and SNMPv2c have similar performance. SNMPv3 has a longer response time, due to the overhead caused by the security services (authentication and privacy). The Pi 3 Model B and Pi 3 Model B+ have comparable performance, and significantly outperform the Pi Zero W.

Novel Approaches to Realize the Reliability of Location Privacy Protocols in Monitoring Wireless Networks

Wireless sensor network (WSN) technology presents significant advantages for Internet of Things (IoT). Sensor-based IoT networks are designed to operate in unattended, harsh, and complex environments. However, WSNs are resource-constrained. Due to the operating environment settings, there exist great challenges in the privacy and reliability of WSN communications. To achieve secure and reliable communications, it is necessary to devise reliable routing protocols and provide a method to evaluate the performance of the protocols. To guarantee location privacy of source nodes, numerous source location privacy (SLP) routing protocols are presented in the literature. However, the existing literature fails to evaluate the SLP reliability of the protocols. This article achieves three main objectives. First, a new relay ring routing (ReRR) protocol is proposed to address some limitations of fake packet-based SLP routing protocols. The routing algorithm of ReRR is specifically designed to provide long-term SLP protection. Second, unlike previous articles that focus solely on measuring the magnitude of SLP protection using performance metrics such as safety period, capture ratio, attack success rate, and capture probability, this article proposes a novel approach to measure the SLP reliability of the protocols. In the third objective, we conduct a series of experiments to analyze the performance of ReRR and fake packet-based protocols. Using the proposed approach, the SLP reliability of the protocols is evaluated. Experiment results reveal that the proposed ReRR protocol exhibits advantageous performance features. It is observed that the fake packet-based routing protocols achieve strong SLP protection by integrating multiple routing techniques such as packet flooding and random distribution of fake packet traffic. However, the achieved SLP protection is short-term and less reliable. On the other hand, the proposed ReRR protocol employs an energy-efficient routing algorithm to guarantee reliability and long-term SLP protection. In addition, the ReRR protocol ensures improved network lifetime.

Congenital heart disease

Congenital heart disease (CHD) is the most common cause of major congenital anomalies, and is the group of malformations that contributes the most for perinatal mortality [1]. It represents an important health care issue and knowing its incidence and risk factors helps developing public and private care policies and clinical protocols. In this paper, researchers published a retrospective analysis from a tertiary hospital in Portugal regarding CHD. In this editorial, we analyze their findings and compare with other publications.

Pseudonym Changing Strategy with Mix Zones Based Authentication Protocol for Location Privacy in Road Networks

A great advancement has been made in intelligent transportation system and communication technologies in order to exchange secure information between automobiles, facilities provider have led an frame over road network. The intelligent transportation system provides an efficient traffic system for drivers, so that there must be less risk to users. In order to design a secure communication protocol among V & V and V & I is a challenging problem. In order to reduce the chance of attacks and increase privacy level, crypto graphic tools provides the feasible solution. In this paper, we proposed pseudonym changing strategy with mix zones (1) Anonymous authentication: the message should be authenticated by issuer due to mix zone and cryptographic tools secure message without any attack. (2) Privacy: Communication contents are confidential due to encrypted messages during communications also improving the scalability through address configuration scheme to reduce computational cost. (3) Efficiency: low storage requirements, The velocity and distance factors may also consider secure measurement, message delivery, overhead and coverage,packet delivery rate, reduce latency and overhead not only by computation cost and time but also compared our scheme, fast delivery rate, low latency and maximum coverage in order to enhance privacy against malicious attacks.

Research on Urban Traffic Signal Control Systems Based on Cyber Physical Systems

In this paper, we present a traffic cyber physical system for urban road traffic signal control, which is referred to as UTSC-CPS. With this proposed system, managers and researchers can realize the construction and simulation of various types of traffic scenarios, the rapid development, and optimization of new control strategies and can apply effective control strategies to actual traffic management. The advantages of this new system include the following. Firstly, the fusion architecture of private cloud computing and edge computing is proposed for the first time, which effectively improves the performance of software and hardware of the urban road traffic signal control system and realizes information security perception and protection in cloud and equipment, respectively, within the fusion framework; secondly, using the concept of parallel system, the depth of real-time traffic control subsystem and real-time simulation subsystem is realized. Thirdly, the idea of virtual scene basic engine and strategy agent engine is put forward in the system design, which separates data from control strategy by designing a general control strategy API and helps researchers focus on control algorithm itself without paying attention to detection data and basic data. Finally, considering China, the system designs a general control strategy API to separate data from control strategy. Most of the popular communication protocols between signal controllers and detectors are private protocols. The standard protocol conversion middleware is skillfully designed, which decouples the field equipment from the system software and achieves the universality and reliability of the control strategy. To further demonstrate the advantages of the new system, we have carried out a one-year practical test in Weifang City, Shandong Province, China. The system has been proved in terms of stability, security, scalability, practicability and rapid practice, and verification of the new control strategy. At the same time, it proves the superiority of the simulation subsystem in the performance and simulation scale by comparing the different-scale road networks of Shunyi District in Beijing and Weifang City in Shandong Province. Further tests were conducted using real intersections, and the results were equally valid.

Efficient distributed privacy-preserving collaborative outlier detection

As a common way to identify anomalous data, outlier detection is widely applicable for intrusions detection, adverse reactions analysis, financial fraud prevention, etc. The accuracy of outlier detection depends crucially on the number of data involved in the test, i.e., the more data participate in detection, the higher accuracy we get. For this reason, cross-dataset collaborative outlier detection is introduced to conquer the lack of data in a single-dataset setting. However, privacy concerns seriously prevent the application of collaborative outlier detection, since most organization are unwilling to share their data with others directly in practice. In this paper, we present efficient protocols for privacy preserving collaborative outlier detection from arbitrarily partitioned data using Local Distance-based Outlier Factor (LDOF). Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who detect outlier on the joint data by secure two-party computation. In particular, we perform arithmetic operations which takes place inside LDOF on arithmetic circuits instead of boolean circuits, and perform sorting operations on boolean circuits. Such a design enables standard operations are performed with suitable circuits, and thus our scheme is more efficient. In addition, to further improve protocol efficiency, local sensitive hash (LSH) is utilized to filter out data which do not need secure computation to reduce the the amount of shared data. We implement our system in C++ on real data. The security analysis and experiments show the security and efficiency of the proposed scheme. Our protocols are more faster than the state of previous methods.

An Anonymous Protocol with User Identification and Linking Capabilities for User Privacy in a Permissioned Blockchain

A permissioned blockchain includes a user in the network after verifying the user’s identity, in contrast to Bitcoin, which is a public blockchain that allows network participation without third-party approval. The two types of permissioned blockchains are private blockchains, each consisting of one server and multiple users, and consortium blockchains, which consist of groups of private blockchains. However, a blockchain has privacy issues, such as user tracking and inference. Therefore, cryptography should be applied for user privacy in a blockchain. There is a lot of research on anonymous protocols for privacy in a blockchain. In this paper, we provide a scheme for user management, i.e., identification and authorization, in a permissioned blockchain. We also propose an anonymous protocol with user identification and transaction linking capabilities provided by the private server, strictly to solve privacy concerns.

Clustering method in protocol reverse engineering for industrial protocols

SummaryAutomation in all aspects of industrial activity is currently needed in today's industries. Networks, which are the most essential elements of automation, have been widely used in industrial sites to realize such needs. However, network security threats and malfunctions at industrial sites can cause considerable physical damage. Damage can be prevented, and threats can be detected through network traffic monitoring. However, industrial protocols use self‐developed protocols to ensure rapid and efficient data transfer, and most self‐developed protocols are private networking protocols. Efficient network traffic monitoring requires a detailed understanding of the structure of industrial protocols. Studies on existing protocol reverse engineering methods for commercial protocols have indicated that there are many limitations in applying these methods to industrial protocols. Therefore, in this paper, we propose a method of analyzing the structure of private protocols that can be employed as industrial protocols. This methodology consists of six modules: traffic collection, message extraction, message clustering by size, message clustering by similarity, field extraction, and session analysis. We collect traffic using the Schneider Modicon M580 and demonstrate the validity of the proposed methodology by comparing collected traffic with existing protocol reverse engineering methods.

The need for privacy with public digital contact tracing during the COVID-19 pandemic

The need for privacy with public digital contact tracing during the COVID-19 pandemic

Quantum protocol for privacy preserving Hamming distance problem of DNA sequences

The privacy preserving Hamming distance problem of DNA sequences is defined and, by using a quantum commutative encryption scheme, a novel quantum protocol for solving it is proposed. A 0-1 coding scheme of DNA sequence is constructed, which transforms the privacy preserving Hamming distance problem of DNA sequences into privacy preserving Hamming distance problem of bit strings. Compared with other related protocols, the efficiency of the proposed protocol is improved, because of entanglement states, joint measurement, unitary operations and even Bell-state measurement are not needed, while only rotation operations and single-state measurement are required, which are easier to be realized with current technology. Furthermore, the correctness and security of the proposed protocol are also analyzed.

Protocol Dissector Tool for Deoding in Band Packet Header on A Switch

Serviceability of networks is a vital part of network management which helps in isolating faults and triaging network issues. Packet analyzers help in identifying faults, security threats and other implementation flaws in the networking software by capturing network traffic and analyzing it. Packet analyzing is heavily based on protocols which need to be decoded from the raw format and presented to the user in an understandable format. In this work, a Command Line Interface based protocol dissector tool has been developed which runs on the operating system of a switch and performs packet decoding by capturing in band packets flowing between control and data plane of the switch. The tool also provides support for packet filtering in order to only capture packets which the user needs. Existing packet dissectors run on Wireshark in the form of Lua plugins. However, in this work the implementation of the entire system is based on C. Some of the public protocols decoded by this tool involve IPv4, IPv6, UDP, TCP, ARP, ICMP and so on. Also, this tool supports decoding of private protocols as well.

DependData: Data collection dependability through three-layer decision-making in BSNs for healthcare monitoring

Recently, there have been extensive studies on applying security and privacy protocols in Body Sensor Networks (BSNs) for patient healthcare monitoring (BSN-Health). Though these protocols provide adequate security to data packets, the collected data may still be compromised at the time of acquisition and before aggregation/storage in the severely resource-constrained BSNs. This leads to data collection frameworks being meaningless or undependable, i.e., an undependable BSN-Health. We study data dependability concerns in the BSN-Health and propose a data dependability verification framework named DependData with the objective of verifying data dependability through the decision-making in three layers. The 1st decision-making (1-DM) layer verifies signal-level data at each health sensor of the BSN locally to guarantee that collected signals ready for processing and transmission are dependable so that undependable processing and transmission in the BSN can be avoided. The 2nd decision-making (2-DM) layer verifies data before aggregation at each local aggregator (like clusterhead) of the BSN to guarantee that data received for aggregation is dependable so that undependable data aggregation can be avoided. The 3rd decision-making (3-DM) layer verifies the stored data before the data appears to a remote healthcare data user to guarantee that data available to the owner end (such as smartphone) is dependable so that undependable information viewing can be avoided. Finally, we evaluate the performance of DependData through simulations regarding 1-DM, 2-DM, and 3-DM and show that up to 92% of data dependability concerns can be detected in the three layers. To the best of our knowledge, DependData would be the first framework to address data dependability aside from current substantial studies of security and privacy protocols. We believe the three layers decision-making framework would attract a wide range of applications in the future.