10 research outputs found

    Decision-making and biases in cybersecurity capability development: Evidence from a simulation game experiment

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    We developed a simulation game to study the effectiveness of decision-makers in overcoming two complexities in building cybersecurity capabilities: potential delays in capability development; and uncertainties in predicting cyber incidents. Analyzing 1479 simulation runs, we compared the performances of a group of experienced professionals with those of an inexperienced control group. Experienced subjects did not understand the mechanisms of delays any better than inexperienced subjects; however, experienced subjects were better able to learn the need for proactive decision-making through an iterative process. Both groups exhibited similar errors when dealing with the uncertainty of cyber incidents. Our findings highlight the importance of training for decision-makers with a focus on systems thinking skills, and lay the groundwork for future research on uncovering mental biases about the complexities of cybersecurity. Keywords: Cybersecurity; Decision-making; Simulation; Capability developmen

    ๋„คํŠธ์›Œํฌ ๊ธฐ๋ฐ˜ ๊ฐ€์ƒ ์‚ฌํšŒ์˜ ์‹ ๋ขฐ ๊ตฌ์ถ• ๋ฒ•์น™๊ณผ ์‹ ๋ขฐ ๊ด€๋ฆฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2012. 8. ํ™ฉ์ค€์„.๊ฐ€์ƒ ์‚ฌํšŒ์—์„œ ์ฐธ์—ฌ์ž๊ฐ€ ์ž…์„ ์ˆ˜ ์žˆ๋Š” ์†์‹ค์€ ํฌ๊ฒŒ ๊ฐ€์ƒ ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ์ผ์›์œผ๋กœ ์—ฌ๊ฒจ์ง€๋Š” ์ฐธ์—ฌ์ž๋กœ๋ถ€ํ„ฐ ์ž…์„ ์ˆ˜ ์žˆ๋Š” ํ”ผํ•ด์™€ ์ปค๋ฎค๋‹ˆํ‹ฐ ์™ธ๋ถ€์˜ ์ƒ๋Œ€๋กœ๋ถ€ํ„ฐ ์ž…์„ ์ˆ˜ ์žˆ๋Š” ํ”ผํ•ด๋กœ ๋‚˜๋‰œ๋‹ค. ๋‹ค์‹œ, ์ปค๋ฎค๋‹ˆํ‹ฐ ์ฐธ์—ฌ์ž๋“ค์— ์˜ํ•ด ๋ฐœ์ƒํ•˜๋Š” ์œ„ํ—˜๊ณผ ๋ถˆํ™•์‹ค์„ฑ์€ ์ƒ๋Œ€๋ฐฉ์˜ ์œ ํ˜•์ด ๊ฐ์ถ”์–ด์ ธ ์žˆ๊ฑฐ๋‚˜ ํ–‰๋™์ด ๊ฐ์ถ”์–ด์ ธ ์žˆ๋Š” ์ •๋ณด๋น„๋Œ€์นญ์œผ๋กœ ์ธํ•ด ๋ฐœ์ƒํ•œ๋‹ค. ๊ฐ€์ƒ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ์ผ์›์œผ๋กœ๋ถ€ํ„ฐ ์ž…์„ ์ˆ˜ ์žˆ๋Š” ํ”ผํ•ด๋Š” ์ผ์ฐจ์ ์œผ๋กœ ๊ตฌ์„ฑ์›๋“ค ๊ฐ„์˜ ์ž์œจ์ ์ธ ๋ฐ˜๋ณต ์†Œํ†ต๊ณผ ํ•™์Šต์„ ํ†ตํ•œ ์ƒ๋Œ€ ์„ ๋ณ„ ๊ธฐ์ค€์˜ ์—…๋ฐ์ดํŠธ๋ฅผ ํ†ตํ•ด ์ ์ฐจ ์–‘์งˆ์˜ ๊ฑฐ๋ž˜ ์ƒ๋Œ€๋ฅผ ์„ ๋ณ„ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์™„ํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํ•œ๋ฒˆ ์„ ๋ณ„๊ณผ์ •์„ ํ†ตํ•ด ์ปค๋ฎค๋‹ˆํ‹ฐ์— ์ง„์ž…ํ•œ ๊ตฌ์„ฑ์›๋„ ๋‹ค์‹œ ๋ฐฐ์‹ ํ•  ์œ ์ธ์€ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ํ‰ํŒ ๋“ฑ ์ง€์†์ ์ธ ๊ตฌ์„ฑ์› ๊ฐ„ ๋ฐ˜๋ณต ์†Œํ†ต ํ˜น์€ ์ด์ฐจ์  ์ˆ˜๋‹จ์ธ ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์ œ3์ž์˜ ๊ฐœ์ž…์„ ํ†ตํ•ด ์™„ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ปค๋ฎค๋‹ˆํ‹ฐ ์™ธ๋ถ€์˜ ์ƒ๋Œ€๋กœ๋ถ€ํ„ฐ ์ž…์„ ์ˆ˜ ์žˆ๋Š” ํ”ผํ•ด๋Š” ์ปค๋ฎค๋‹ˆํ‹ฐ ์ „์ฒด ์ˆ˜์ค€์—์„œ ๊ธฐ์ˆ ์ ์œผ๋กœ ๋ฐฉ์–ดํ•˜๊ฑฐ๋‚˜, ํ˜น์€ ๋™์‹œ์— ๊ฐœ์ธ์ด ํ”ผํ•ด๋ฅผ ์˜ˆ๋ฐฉํ•  ์ˆ˜ ์žˆ๋Š” ํˆฌ์ž๋ฅผ ์„ ํ–‰ํ•จ์œผ๋กœ์จ ์™„ํ™”ํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€์ƒ ์‚ฌํšŒ์—์„œ ์ฐธ์—ฌ์ž์˜ ์†์‹ค ์™„ํ™”์™€ ์˜ˆ๋ฐฉ, ๊ทธ๋ฆฌ๊ณ  ๋ฐฉ์–ด๋ฅผ ์œ„ํ•œ ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ๊ตฌ์„ฑ ๊ธฐ์ค€, ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ๊ด€๋ฆฌ ๋ฐฉ๋ฒ•, ๊ทธ๋ฆฌ๊ณ  ์™ธ๋ถ€๋กœ๋ถ€ํ„ฐ์˜ ๋ฐฉ์–ด๋ฅผ ์œ„ํ•œ ํˆฌ์ž๋ฅผ ์‚ฌํšŒ์™€ ๊ฐœ์ธ์˜ ํšจ์œจ์„ฑ ํ™•๋ณด๋ผ๋Š” ์ธก๋ฉด์—์„œ ์ ‘๊ทผํ•˜์—ฌ ์ตœ์„ ์˜ ์ •์ฑ…์„ ์ œ์•ˆํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์šฐ์„  ์ธํ„ฐ๋„ท ๊ธฐ๋ฐ˜์˜ ๊ฐ€์ƒ ์‚ฌํšŒ์—์„œ ์ฐธ์—ฌ์ž๋“ค์ด ์ƒ๋Œ€๋ฅผ ์„ ๋ณ„ํ•˜์—ฌ ์ปค๋ฎค๋‹ˆํ‹ฐ๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ๊ธฐ์ค€์œผ๋กœ ์‹ ๋ขฐ ์‹ ํ˜ธ ๊ฒŒ์ž„ ๋ชจ๋ธ์„ ๊ตฌ์„ฑํ•˜๊ณ , ์‹ ํ˜ธ๋งŒ์œผ๋กœ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์ƒ๋Œ€๋ฅผ ํŒ๋ณ„ํ•  ์ˆ˜ ํ•˜๊ธฐ ์œ„ํ•œ ์‹ ํ˜ธ๋น„์šฉ ๊ธฐ๋ฐ˜ ์‹œ์Šคํ…œ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ์‹ ํ˜ธ๋ฅผ ํ†ตํ•ด ์ƒ๋Œ€๋ฅผ ํŒ๋ณ„ํ•˜์—ฌ ๊ฑฐ๋ž˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์ผ๋ถ€ ์กฐ๊ฑด์—์„œ๋Š” ์‚ฌํšŒ์ ์œผ๋กœ ์ตœ์ ์€ ์•„๋‹ˆ๋ฉฐ, ์‚ฌํšŒ ํ›„์ƒ ๊ด€์ ์—์„œ ํšจ์œจ์ ์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‹ ํ˜ธ์ฒด๊ณ„์˜ ์„ค๊ณ„ ํ˜น์€ ์‹ ๋ขฐ์„ฑ ๋‚ฎ์€ ์‚ฌ์šฉ์ž์— ๋Œ€ํ•œ ๊ทœ์ œ๋ผ๋Š” ๋‘ ์ •์ฑ… ์ค‘์—์„œ ์„ ํƒํ•ด์•ผ ํ•จ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ์ƒ๋Œ€๋ฅผ ์„ ํƒํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜์–ด ์˜จ ๋ฐฉ๋ฒ•์ธ ํ‰ํŒ์€ ์ผ์ข…์˜ ์‹ ํ˜ธ์ฒด๊ณ„๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ, ์šฐ์„  ์‹ ํ˜ธ ์ฒด๊ณ„๋ฅผ ํ†ตํ•ด ์ปค๋ฎค๋‹ˆํ‹ฐ์— ์ง„์ž…ํ•œ ์ดํ›„์—๋Š” ์ด๋ฅผ ๊ด€๋ฆฌํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•ด์ง„๋‹ค. ์ด ๊ณผ์ •์—์„œ ์ง‘ํ•ฉ์ ์ธ ์‹ ๋ขฐ์˜ ๋ณด์žฅ๊ณผ ๊ฐœ์ธ์˜ ํ”„๋ผ์ด๋ฒ„์‹œ ๋ณดํ˜ธ ๊ฐ„์˜ ๊ธด์žฅ๊ด€๊ณ„๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋ชจ๋‹ˆํ„ฐ๋ง๊ณผ ์ฒ˜๋ฒŒ์ด๋ผ๋Š” ์ •์ฑ… ๋ณ€์ˆ˜๋ฅผ ์ œ์•ˆํ•˜๊ณ , ์ตœ์  ์ˆ˜์ค€์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๊ฑฐ๋ž˜ ํ˜น์€ ์†Œํ†ตํ•˜๋Š” ์ „์ฒด ์ปค๋ฎค๋‹ˆํ‹ฐ์˜ ๊ฑด์ „์„ฑ ์œ ์ง€๋ฅผ ์œ„ํ•ด์„œ ๋„์ž…๋˜๋Š” ์‹ ๋ขฐ๋ฐ›๋Š” ์ œ3์ž์˜ ์ •์ฑ…์ด ์ค‘์š”ํ•˜๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ฐœ์ธ์ด ์ž์‹ ์˜ ํ”„๋ผ์ด๋ฒ„์‹œ๋ฅผ ์ง€ํ‚ค๊ธฐ ์œ„ํ•œ ํˆฌ์ž๋ฅผ ํ•˜๋Š” ์ƒํ™ฉ์—์„œ ์„œ๋กœ ๊ฑฐ๋ž˜ํ•  ๋•Œ์—, ๋ฐฉ์–ด์ž์˜ ์ž…์žฅ์—์„œ ์ „๋žต์„ ์„ธ์šธ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณต๊ฒฉ์ž ๋˜ํ•œ ํ•˜๋‚˜์˜ ๊ฒŒ์ž„ ์ฐธ์—ฌ์ž๋ผ๋Š” ์ž…์žฅ์—์„œ ์ด๋“ค์˜ ์œ ์ธ์„ ๊ณ ๋ คํ•œ ์˜์‚ฌ๊ฒฐ์ • ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฒŒ์ž„์˜ ๋ถ„์„์  ๋ชจ๋ธ๊ณผ ์‹คํ—˜ ๋ชจ๋ธ์„ ํ†ตํ•ด ์–ป์–ด์ง„ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, ์†์‹ค์— ๋Œ€ํ•œ ๊ณต๊ฒฉ์ž์˜ ์ด์ต ๋น„์œจ์ด ์ƒ๋Œ€์ ์œผ๋กœ ํด ์ˆ˜๋ก ๋ณด์•ˆํˆฌ์ž์— ๋Œ€ํ•œ ๊ธฐ๋Œ€์ด์ต์ด ์ค„์–ด๋“ ๋‹ค. ๋ณด์•ˆ ํˆฌ์ž๋ฅผ ํ†ตํ•œ ๊ธฐ๋Œ€ ์ด์ต์„ ๊ทน๋Œ€ํ™”ํ•˜๋Š” ์ตœ์  ํˆฌ์ž๋Ÿ‰๊ณผ ํˆฌ์ž ์‹œ์ ์„ ๋˜ํ•œ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์˜ˆ์ƒ๋˜๋Š” ๊ณต๊ฒฉ์˜ ์ข…๋ฅ˜์™€ ํ™•๋ฅ ์— ๋”ฐ๋ผ ์ ์ ˆํ•œ ๋ณด์•ˆํˆฌ์ž ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค๋Š” ๊ฒฐ๋ก ์„ ์ œ์‹œํ•˜๊ณ , ๋ช‡ ๊ฐ€์ง€ ์‚ฌ๋ก€์— ๋Œ€ํ•ด ๊ฐ€์ƒ ํฌํŠธํด๋ฆฌ์˜ค๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค.The uncertainties and risks in a virtual society can be divided into those posed by a member of the community and those posed by an outsider of the community. The uncertainties and risks from a member of the community can be further divided into those stemming from the hidden type problem and those stemming from the hidden action problem in the context of information asymmetry. These uncertainties and risks posed by community members can be alleviated by a prudently designed selection mechanism that uses repeated communication and learning. Nevertheless, there exists an incentive to commit a violation for a community member who is selected by the selection mechanism. A complementary mechanism such as reputation or third party intervention is therefore required to resolve this problem. On the other hand, the alleviation of the uncertainty or risk posed by an outsider of the community requires the effort of the entire community and individual investment by each community member to protect their information and systems. Enhancing trust is a critical factor in the development of virtual and offline societies. Just as various policy tools have been used continuously to build trust in the real society, various policy guidelines also need to be suggested to build trust in the virtual society. Although previous studies have focused on suggesting policy guidelines based on observed phenomena, this study provides the theoretical foundation for analyzing the process of trust building in various environments of virtual society using the game theory approach. The theoretical analysis in this research suggests that the most critical task is to make a pool of trustworthy providers to establish an efficient market. Prudent policies also need to be designed to differentiate the signaling costs for different types of providers. The trusted third party method can be one of the possible alternatives. As this study suggests, even in a trustworthy market, minimum monitoring and penalty contracts are necessary and individual users need to invest in optimal security. This research also contributes to the development of a new trust-management mechanism that is not only more objective and robust but also has a simple structure that can be easily understood by users in the virtual society. Existing studies have merely focused on one of the two conflicting values or indicated the limitations of pervasive reputation mechanisms. Moreover, flexible-monitoring levels cannot be chosen when the service participants are highly concerned about their privacy or when the expected loss from the invasion of privacy is high. In such cases, the level of punishment is inevitably highlegal enforcement is therefore required to complement the voluntary punishment scheme for virtual society models such as the utility-computing service market. Finally, this research contributes to the decision-making process of the defender. The proposed model gives a defender more practical instruments to decide the optimal level of security investment through consideration of the attackers strategic decisions. The majority of existing studies have considered only the defenders perspective and have regarded the actions of attackers as a given. The last analysis suggested a model of interdependent decision-making processes of two players behaving strategically. The strategic attacker bases its strategies such as attack frequency on the actions of the defender, whereas the strategic defender bases its strategies such as the level of security investment on the actions of the attacker. The model used in this study aims to provide the defender more practical instruments to determine the optimal level of security investment through the consideration of the attackers decision-making process.Chapter 1. Introduction 1 1.1 Research Background 1 1.1.1 The characteristics of a virtual society 1 1.1.2 Approaches to investigation of virtual society 3 1.2 Problem Statement 6 1.3 Approach and Conceptual Framework 10 1.4 Research Questions 15 1.5 Outlines of the study 17 Chapter 2. Literature Review 21 2.1 Research on the trust building in various context 21 2.1.1 Social dilemmas and the trust 21 2.1.2 Traditional and behavioral approaches of game theory 22 2.1.3 Trust concepts in various contexts 23 2.2 Mechanisms to develop trustworthy environment 26 2.2.1 Signaling game approaches 28 2.2.2 Prisoners dilemma game approaches 30 2.2.3 Trusted third party interventions 34 2.2.4 Private solutions by individual dimension 36 2.2.5 Other game theoretical approaches 38 2.3 Agent based simulation 38 Chapter 3. Trust Signaling Game as a Fundamental Rule of Transactions on the Internet Based Virtual Society 40 3.1 Introduction 40 3.2 Model Description 41 3.3 Equilibrium Analysis 45 3.3.1 Separating equilibrium 45 3.3.2 Pooling equilibrium 47 3.3.3 The existence condition of the equilibrium 50 3.3.4 The social optimality of the equilibrium 51 3.3.5 The continuous needs of costly signals 53 3.3.6 The dynamics of the trust equilibrium shifts 53 3.4 The Simulation and Results 54 3.4.1 The simulation overview 54 3.4.2 The simulation description 56 3.4.3 The simulation results 60 3.4.4 The comparison with equilibrium analysis 63 3.5 Conclusion and Discussion 63 Chapter 4. Balancing between Privacy Protection and Security Robustness 66 4.1 Introduction 66 4.2 Motivation and Related Works 68 4.2.1 Prisoners dilemma 68 4.2.2 Demerits of the reputation mechanism 70 4.3 Model Description 75 4.3.1 Game Design 78 4.3.2 Investment in privacy protection 82 4.3.3 Implications 85 4.4 Simulation 85 4.4.1 Simulation Architecture 86 4.4.2 Simulation Results 89 4.5 Model Validation and Adaptation 93 4.5.1 Robustness against unfair or biased ratings 93 4.5.2 Long-term accuracy of the trust level 94 4.5.3 Validation and Sensitivity test 98 4.6 Conclusion and Discussion 100 Chapter 5. Modeling the Defenders Strategic Decision Process in Security Investment 102 5.1 Introduction 102 5.2 Model 103 5.2.1 Motivation 103 5.2.2 Attackers behavior 106 5.2.3 Defenders behavior 110 5.3 Equilibrium Analysis 112 5.3.1 Simultaneous game 113 5.3.2 Sequential game 116 5.3.3 Comparison of the equilibriums 119 5.4 Comparative Static 120 5.5 Conclusion and Discussion 126 Chapter 6. Discussion and Policy Implication 131 6.1 Results Summary and Discussion 131 6.2 Contributions and Policy Implications 133 6.3 Future Research 136Docto

    Exploring the influence of organisational, environmental, and technological factors on information security policies and compliance at South African higher education institutions: Implications for biomedical research.

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    >Magister Scientiae - MScHeadline reports on data breaches worldwide have resulted in heightened concerns about information security vulnerability. In Africa, South Africa is ranked among the top โ€˜at-riskโ€™ countries with information security vulnerabilities and is the most the most cybercrime-targeted country. Globally, such cyber vulnerability incidents greatly affect the education sector, due, in part, to the fact that it holds more Personal Identifiable Information (PII) than other sectors. PII refers to (but is not limited to) ID numbers, financial account numbers, and biomedical research data. In response to rising threats, South Africa has implemented a regulation called the Protection of Personal Information Act (POPIA), similar to the European Union General Data Protection Regulation (GDPR), which seeks to mitigate cybercrime and information security vulnerabilities. The extent to which African institutions, especially in South Africa, have embraced and responded to these two information security regulations remains vague, making it a crucial matter for biomedical researchers. This study aimed to assess whether the participating universities have proper and reliable information security practices, measures and management in place and whether they fall in line with both national (POPIA) and international (GDPR) regulations. In order to achieve this aim, the study undertook a qualitative exploratory analysis of information security management across three universities in South Africa. A Technology, Organizational, and Environmental (TOE) model was employed to investigate factors that may influence effective information security measures. A Purposeful sampling method was employed to interview participants from each university. From the technological standpoint, Bring Your Own Device (BYOD) policy, whereby on average, a student owns and connects between three to four internet-enabled devices to the network, has created difficulties for IT teams, particularly in the areas of authentication, explosive growth in bandwidth, and access control to security university servers. In order to develop robust solutions to mitigate these concerns, and which are not perceived by users as overly prohibitive, executive management should acknowledge that security and privacy issues are a universal problem and not solely an IT problem and equip the IT teams with the necessary tools and mechanisms to allow them to overcome commonplace challenges. At an organisational level, information security awareness training of all users within the university setting was identified as a key factor in protecting the integrity, confidentiality, and availability of information in highly networked environments. Furthermore, the Universityโ€™s information security mission must not simply be a link on a website, it should be constantly re-enforced by informing users during, and after, the awareness training. In terms of environmental factors, specifically the GDPR and POPIA legislations, one of the most practical and cost-effective ways universities can achieve data compliance requirements is to help staff (both teaching and non-teaching), students, and other employees understand the business value of all information. Users which are more aware of sensitivity of data, risks to the data, and their responsibilities when handling, storing, processing, and distributing data during their day to day activities will behave in a manner that would makes compliance easier at the institutional level. Results obtained in this study helped to elucidate the current status, issues, and challenges which universities are facing in the area of information security management and compliance, particularly in the South African context. Findings from this study point to organizational factors being the most critical when compared to the technological and environmental contexts examined. Furthermore, several proposed information security policies were developed with a view to assist biomedical practitioners within the institutional setting in protecting sensitive biomedical data
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