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2021-02-03
Bellas, A., Perrin, S., Malone, B., Rogers, K., Lucas, G., Phillips, E., Tossell, C., Visser, E. d.  2020.  Rapport Building with Social Robots as a Method for Improving Mission Debriefing in Human-Robot Teams. 2020 Systems and Information Engineering Design Symposium (SIEDS). :160—163.

Conflicts may arise at any time during military debriefing meetings, especially in high intensity deployed settings. When such conflicts arise, it takes time to get everyone back into a receptive state of mind so that they engage in reflective discussion rather than unproductive arguing. It has been proposed by some that the use of social robots equipped with social abilities such as emotion regulation through rapport building may help to deescalate these situations to facilitate critical operational decisions. However, in military settings, the same AI agent used in the pre-brief of a mission may not be the same one used in the debrief. The purpose of this study was to determine whether a brief rapport-building session with a social robot could create a connection between a human and a robot agent, and whether consistency in the embodiment of the robot agent was necessary for maintaining this connection once formed. We report the results of a pilot study conducted at the United States Air Force Academy which simulated a military mission (i.e., Gravity and Strike). Participants' connection with the agent, sense of trust, and overall likeability revealed that early rapport building can be beneficial for military missions.

Xu, J., Howard, A..  2020.  How much do you Trust your Self-Driving Car? Exploring Human-Robot Trust in High-Risk Scenarios 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :4273—4280.

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants’ decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants’ trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

Lyons, J. B., Nam, C. S., Jessup, S. A., Vo, T. Q., Wynne, K. T..  2020.  The Role of Individual Differences as Predictors of Trust in Autonomous Security Robots. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—5.

This research used an Autonomous Security Robot (ASR) scenario to examine public reactions to a robot that possesses the authority and capability to inflict harm on a human. Individual differences in terms of personality and Perfect Automation Schema (PAS) were examined as predictors of trust in the ASR. Participants (N=316) from Amazon Mechanical Turk (MTurk) rated their trust of the ASR and desire to use ASRs in public and military contexts following a 2-minute video depicting the robot interacting with three research confederates. The video showed the robot using force against one of the three confederates with a non-lethal device. Results demonstrated that individual differences factors were related to trust and desired use of the ASR. Agreeableness and both facets of the PAS (high expectations and all-or-none beliefs) demonstrated unique associations with trust using multiple regression techniques. Agreeableness, intellect, and high expectations were uniquely related to desired use for both public and military domains. This study showed that individual differences influence trust and one's desired use of ASRs, demonstrating that societal reactions to ASRs may be subject to variation among individuals.

Xu, J., Howard, A..  2020.  Would you Take Advice from a Robot? Developing a Framework for Inferring Human-Robot Trust in Time-Sensitive Scenarios 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :814—820.

Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed.

Alarcon, G. M., Gibson, A. M., Jessup, S. A..  2020.  Trust Repair in Performance, Process, and Purpose Factors of Human-Robot Trust. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—6.

The current study explored the influence of trust and distrust behaviors on performance, process, and purpose (trustworthiness) perceptions over time when participants were paired with a robot partner. We examined the changes in trustworthiness perceptions after trust violations and trust repair after those violations. Results indicated performance, process, and purpose perceptions were all affected by trust violations, but perceptions of process and purpose decreased more than performance following a distrust behavior. Similarly, trust repair was achieved in performance perceptions, but trust repair in perceived process and purpose was absent. When a trust violation occurred, process and purpose perceptions deteriorated and failed to recover from the violation. In addition, the trust violation resulted in untrustworthy perceptions of the robot. In contrast, trust violations decreased partner performance perceptions, and subsequent trust behaviors resulted in a trust repair. These findings suggest that people are more sensitive to distrust behaviors in their perceptions of process and purpose than they are in performance perceptions.

Rossi, A., Dautenhahn, K., Koay, K. Lee, Walters, M. L..  2020.  How Social Robots Influence People’s Trust in Critical Situations. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :1020—1025.

As we expect that the presence of autonomous robots in our everyday life will increase, we must consider that people will have not only to accept robots to be a fundamental part of their lives, but they will also have to trust them to reliably and securely engage them in collaborative tasks. Several studies showed that robots are more comfortable interacting with robots that respect social conventions. However, it is still not clear if a robot that expresses social conventions will gain more favourably people's trust. In this study, we aimed to assess whether the use of social behaviours and natural communications can affect humans' sense of trust and companionship towards the robots. We conducted a between-subjects study where participants' trust was tested in three scenarios with increasing trust criticality (low, medium, high) in which they interacted either with a social or a non-social robot. Our findings showed that participants trusted equally a social and non-social robot in the low and medium consequences scenario. On the contrary, we observed that participants' choices of trusting the robot in a higher sensitive task was affected more by a robot that expressed social cues with a consequent decrease of their trust in the robot.

Razin, Y. S., Feigh, K. M..  2020.  Hitting the Road: Exploring Human-Robot Trust for Self-Driving Vehicles. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—6.

With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles.

Mou, W., Ruocco, M., Zanatto, D., Cangelosi, A..  2020.  When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :956—962.

Trust is a critical issue in human-robot interactions (HRI) as it is the core of human desire to accept and use a non-human agent. Theory of Mind (ToM) has been defined as the ability to understand the beliefs and intentions of others that may differ from one's own. Evidences in psychology and HRI suggest that trust and ToM are interconnected and interdependent concepts, as the decision to trust another agent must depend on our own representation of this entity's actions, beliefs and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we investigated whether the exposure to the ToM abilities of a robot could affect humans' trust towards the robot. To this end, participants played a Price Game with a humanoid robot (Pepper) that was presented having either low-level ToM or high-level ToM. Specifically, the participants were asked to accept the price evaluations on common objects presented by the robot. The willingness of the participants to change their own price judgement of the objects (i.e., accept the price the robot suggested) was used as the main measurement of the trust towards the robot. Our experimental results showed that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills.

Ye, S., Feigh, K., Howard, A..  2020.  Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :1186—1189.

Embodiment of actions and tasks has typically been analyzed from the robot's perspective where the robot's embodiment helps develop and maintain trust. However, we ask a similar question looking at the interaction from the human perspective. Embodied cognition has been shown in the cognitive science literature to produce increased social empathy and cooperation. To understand how human embodiment can help develop and increase trust in human-robot interactions, we created conducted a study where participants were tasked with memorizing greek letters associated with dance motions with the help of a humanoid robot. Participants either performed the dance motion or utilized a touch screen during the interaction. The results showed that participants' trust in the robot increased at a higher rate during human embodiment of motions as opposed to utilizing a touch screen device.

Velaora, M., Roy, R. van, Guéna, F..  2020.  ARtect, an augmented reality educational prototype for architectural design. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :110—115.

ARtect is an Augmented Reality application developed with Unity 3D, which envisions an educational interactive and immersive tool for architects, designers, researchers, and artists. This digital instrument renders the competency to visualize custom-made 3D models and 2D graphics in interior and exterior environments. The user-friendly interface offers an accurate insight before the materialization of any architectural project, enabling evaluation of the design proposal. This practice could be integrated into learning architectural design process, saving resources of printed drawings, and 3D carton models during several stages of spatial conception.

Clark, D. J., Turnbull, B..  2020.  Experiment Design for Complex Immersive Visualisation. 2020 Military Communications and Information Systems Conference (MilCIS). :1—5.

Experimentation focused on assessing the value of complex visualisation approaches when compared with alternative methods for data analysis is challenging. The interaction between participant prior knowledge and experience, a diverse range of experimental or real-world data sets and a dynamic interaction with the display system presents challenges when seeking timely, affordable and statistically relevant experimentation results. This paper outlines a hybrid approach proposed for experimentation with complex interactive data analysis tools, specifically for computer network traffic analysis. The approach involves a structured survey completed after free engagement with the software platform by expert participants. The survey captures objective and subjective data points relating to the experience with the goal of making an assessment of software performance which is supported by statistically significant experimental results. This work is particularly applicable to field of network analysis for cyber security and also military cyber operations and intelligence data analysis.

Aliman, N.-M., Kester, L..  2020.  Malicious Design in AIVR, Falsehood and Cybersecurity-oriented Immersive Defenses. 2020 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). :130—137.

Advancements in the AI field unfold tremendous opportunities for society. Simultaneously, it becomes increasingly important to address emerging ramifications. Thereby, the focus is often set on ethical and safe design forestalling unintentional failures. However, cybersecurity-oriented approaches to AI safety additionally consider instantiations of intentional malice – including unethical malevolent AI design. Recently, an analogous emphasis on malicious actors has been expressed regarding security and safety for virtual reality (VR). In this vein, while the intersection of AI and VR (AIVR) offers a wide array of beneficial cross-fertilization possibilities, it is responsible to anticipate future malicious AIVR design from the onset on given the potential socio-psycho-technological impacts. For a simplified illustration, this paper analyzes the conceivable use case of Generative AI (here deepfake techniques) utilized for disinformation in immersive journalism. In our view, defenses against such future AIVR safety risks related to falsehood in immersive settings should be transdisciplinarily conceived from an immersive co-creation stance. As a first step, we motivate a cybersecurity-oriented procedure to generate defenses via immersive design fictions. Overall, there may be no panacea but updatable transdisciplinary tools including AIVR itself could be used to incrementally defend against malicious actors in AIVR.

Lee, J..  2020.  CanvasMirror: Secure Integration of Third-Party Libraries in a WebVR Environment. 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). :75—76.

Web technology has evolved to offer 360-degree immersive browsing experiences. This new technology, called WebVR, enables virtual reality by rendering a three-dimensional world on an HTML canvas. Unfortunately, there exists no browser-supported way of sharing this canvas between different parties. As a result, third-party library providers with ill intent (e.g., stealing sensitive information from end-users) can easily distort the entire WebVR site. To mitigate the new threats posed in WebVR, we propose CanvasMirror, which allows publishers to specify the behaviors of third-party libraries and enforce this specification. We show that CanvasMirror effectively separates the third-party context from the host origin by leveraging the privilege separation technique and safely integrates VR contents on a shared canvas.

Bahaei, S. Sheikh.  2020.  A Framework for Risk Assessment in Augmented Reality-Equipped Socio-Technical Systems. 2020 50th Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S). :77—78.

New technologies, such as augmented reality (AR) are used to enhance human capabilities and extend human functioning; nevertheless they may cause distraction and incorrect human functioning. Systems including socio entities (such as human) and technical entities (such as augmented reality) are called socio-technical systems. In order to do risk assessment in such systems, considering new dependability threats caused by augmented reality is essential, for example failure of an extended human function is a new type of dependability threat introduced to the system because of new technologies. In particular, it is required to identify these new dependability threats and extend modeling and analyzing techniques to be able to uncover their potential impacts. This research aims at providing a framework for risk assessment in AR-equipped socio-technical systems by identifying AR-extended human failures and AR-caused faults leading to human failures. Our work also extends modeling elements in an existing metamodel for modeling socio-technical systems, to enable AR-relevant dependability threats modeling. This extended metamodel is expected to be used for extending analysis techniques to analyze AR-equipped socio-technical systems.

Sabu, R., Yasuda, K., Kato, R., Kawaguchi, S., Iwata, H..  2020.  Does visual search by neck motion improve hemispatial neglect?: An experimental study using an immersive virtual reality system 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :262—267.

Unilateral spatial neglect (USN) is a higher cognitive dysfunction that can occur after a stroke. It is defined as an impairment in finding, reporting, reacting to, and directing stimuli opposite the damaged side of the brain. We have proposed a system to identify neglected regions in USN patients in three dimensions using three-dimensional virtual reality. The objectives of this study are twofold: first, to propose a system for numerically identifying the neglected regions using an object detection task in a virtual space, and second, to compare the neglected regions during object detection when the patient's neck is immobilized (‘fixed-neck’ condition) versus when the neck can be freely moved to search (‘free-neck’ condition). We performed the test using an immersive virtual reality system, once with the patient's neck fixed and once with the patient's neck free to move. Comparing the results of the study in two patients, we found that the neglected areas were similar in the fixed-neck condition. However, in the free-neck condition, one patient's neglect improved while the other patient’s neglect worsened. These results suggest that exploratory ability affects the symptoms of USN and is crucial for clinical evaluation of USN patients.

Powley, B. T..  2020.  Exploring Immersive and Non-Immersive Techniques for Geographic Data Visualization. 2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). :1—2.

Analyzing multi-dimensional geospatial data is difficult and immersive analytics systems are used to visualize geospatial data and models. There is little previous work evaluating when immersive and non-immersive visualizations are the most suitable for data analysis and more research is needed.

Kennard, M., Zhang, H., Akimoto, Y., Hirokawa, M., Suzuki, K..  2020.  Effects of Visual Biofeedback on Competition Performance Using an Immersive Mixed Reality System. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). :3793—3798.

This paper investigates the effects of real time visual biofeedback for improving sports performance using a large scale immersive mixed reality system in which users are able to play a simulated game of curling. The users slide custom curling stones across the floor onto a projected target whose size is dictated by the user’s stress-related physiological measure; heart rate (HR). The higher HR the player has, the smaller the target will be, and vice-versa. In the experiment participants were asked to compete in three different conditions: baseline, with and without the proposed biofeedback. The results show that when providing a visual representation of the player’s HR or "choking" in competition, it helped the player understand their condition and improve competition performance (P-value of 0.0391).

Illing, B., Westhoven, M., Gaspers, B., Smets, N., Brüggemann, B., Mathew, T..  2020.  Evaluation of Immersive Teleoperation Systems using Standardized Tasks and Measurements. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :278—285.

Despite advances regarding autonomous functionality for robots, teleoperation remains a means for performing delicate tasks in safety critical contexts like explosive ordnance disposal (EOD) and ambiguous environments. Immersive stereoscopic displays have been proposed and developed in this regard, but bring about their own specific problems, e.g., simulator sickness. This work builds upon standardized test environments to yield reproducible comparisons between different robotic platforms. The focus was placed on testing three optronic systems of differing degrees of immersion: (1) A laptop display showing multiple monoscopic camera views, (2) an off-the-shelf virtual reality headset coupled with a pantilt-based stereoscopic camera, and (3) a so-called Telepresence Unit, providing fast pan, tilt, yaw rotation, stereoscopic view, and spatial audio. Stereoscopic systems yielded significant faster task completion only for the maneuvering task. As expected, they also induced Simulator Sickness among other results. However, the amount of Simulator Sickness varied between both stereoscopic systems. Collected data suggests that a higher degree of immersion combined with careful system design can reduce the to-be-expected increase of Simulator Sickness compared to the monoscopic camera baseline while making the interface subjectively more effective for certain tasks.

Cecotti, H., Richard, Q., Gravellier, J., Callaghan, M..  2020.  Magnetic Resonance Imaging Visualization in Fully Immersive Virtual Reality. 2020 6th International Conference of the Immersive Learning Research Network (iLRN). :205—209.

The availability of commercial fully immersive virtual reality systems allows the proposal and development of new applications that offer novel ways to visualize and interact with multidimensional neuroimaging data. We propose a system for the visualization and interaction with Magnetic Resonance Imaging (MRI) scans in a fully immersive learning environment in virtual reality. The system extracts the different slices from a DICOM file and presents the slices in a 3D environment where the user can display and rotate the MRI scan, and select the clipping plane in all the possible orientations. The 3D environment includes two parts: 1) a cube that displays the MRI scan in 3D and 2) three panels that include the axial, sagittal, and coronal views, where it is possible to directly access a desired slice. In addition, the environment includes a representation of the brain where it is possible to access and browse directly through the slices with the controller. This application can be used both for educational purposes as an immersive learning tool, and by neuroscience researchers as a more convenient way to browse through an MRI scan to better analyze 3D data.

Martin, S., Parra, G., Cubillo, J., Quintana, B., Gil, R., Perez, C., Castro, M..  2020.  Design of an Augmented Reality System for Immersive Learning of Digital Electronic. 2020 XIV Technologies Applied to Electronics Teaching Conference (TAEE). :1—6.

This article describes the development of two mobile applications for learning Digital Electronics. The first application is an interactive app for iOS where you can study the different digital circuits, and which will serve as the basis for the second: a game of questions in augmented reality.

Ani, U. D., He, H., Tiwari, A..  2020.  Vulnerability-Based Impact Criticality Estimation for Industrial Control Systems. 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1—8.

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.

2021-02-01
Ajenaghughrure, I. B., Sousa, S. C. da Costa, Lamas, D..  2020.  Risk and Trust in artificial intelligence technologies: A case study of Autonomous Vehicles. 2020 13th International Conference on Human System Interaction (HSI). :118–123.
This study investigates how risk influences users' trust before and after interactions with technologies such as autonomous vehicles (AVs'). Also, the psychophysiological correlates of users' trust from users” eletrodermal activity responses. Eighteen (18) carefully selected participants embark on a hypothetical trip playing an autonomous vehicle driving game. In order to stay safe, throughout the drive experience under four risk conditions (very high risk, high risk, low risk and no risk) that are based on automotive safety and integrity levels (ASIL D, C, B, A), participants exhibit either high or low trust by evaluating the AVs' to be highly or less trustworthy and consequently relying on the Artificial intelligence or the joystick to control the vehicle. The result of the experiment shows that there is significant increase in users' trust and user's delegation of controls to AVs' as risk decreases and vice-versa. In addition, there was a significant difference between user's initial trust before and after interacting with AVs' under varying risk conditions. Finally, there was a significant correlation in users' psychophysiological responses (electrodermal activity) when exhibiting higher and lower trust levels towards AVs'. The implications of these results and future research opportunities are discussed.
Gupta, K., Hajika, R., Pai, Y. S., Duenser, A., Lochner, M., Billinghurst, M..  2020.  Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). :756–765.
With the advancement of Artificial Intelligence technology to make smart devices, understanding how humans develop trust in virtual agents is emerging as a critical research field. Through our research, we report on a novel methodology to investigate user's trust in auditory assistance in a Virtual Reality (VR) based search task, under both high and low cognitive load and under varying levels of agent accuracy. We collected physiological sensor data such as electroencephalography (EEG), galvanic skin response (GSR), and heart-rate variability (HRV), subjective data through questionnaire such as System Trust Scale (STS), Subjective Mental Effort Questionnaire (SMEQ) and NASA-TLX. We also collected a behavioral measure of trust (congruency of users' head motion in response to valid/ invalid verbal advice from the agent). Our results indicate that our custom VR environment enables researchers to measure and understand human trust in virtual agents using the matrices, and both cognitive load and agent accuracy play an important role in trust formation. We discuss the implications of the research and directions for future work.
Li, R., Ishimaki, Y., Yamana, H..  2020.  Privacy Preserving Calculation in Cloud using Fully Homomorphic Encryption with Table Lookup. 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). :315–322.
To protect data in cloud servers, fully homomorphic encryption (FHE) is an effective solution. In addition to encrypting data, FHE allows a third party to evaluate arithmetic circuits (i.e., computations) over encrypted data without decrypting it, guaranteeing protection even during the calculation. However, FHE supports only addition and multiplication. Functions that cannot be directly represented by additions or multiplications cannot be evaluated with FHE. A naïve implementation of such arithmetic operations with FHE is a bit-wise operation that encrypts numerical data as a binary string. This incurs huge computation time and storage costs, however. To overcome this limitation, we propose an efficient protocol to evaluate multi-input functions with FHE using a lookup table. We extend our previous work, which evaluates a single-integer input function, such as f(x). Our extended protocol can handle multi-input functions, such as f(x,y). Thus, we propose a new method of constructing lookup tables that can evaluate multi-input functions to handle general functions. We adopt integer encoding rather than bit-wise encoding to speed up the evaluations. By adopting both permutation operations and a private information retrieval scheme, we guarantee that no information from the underlying plaintext is leaked between two parties: a cloud computation server and a decryptor. Our experimental results show that the runtime of our protocol for a two-input function is approximately 13 minutes, when there are 8,192 input elements in the lookup table. By adopting a multi-threading technique, the runtime can be further reduced to approximately three minutes with eight threads. Our work is more practical than a previously proposed bit-wise implementation, which requires 60 minutes to evaluate a single-input function.
Nakadai, N., Iseki, T., Hayashi, M..  2020.  Improving the Security Strength of Iseki’s Fully Homomorphic Encryption. 2020 35th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). :299–304.
This paper proposes a method that offers much higher security for Iseki's fully homomorphic encryption (FHE), a recently proposed secure computation scheme. The key idea is re-encrypting already encrypted data. This second encryption is executed using new common keys, whereby two or more encryptions offer much stronger security.