Human Factors in Developing Trustworthy Service-Based Systems

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Human Factors in Developing Trustworthy
Service-Based Systems

• Stephen S. Yau
• Information Assurance Center, and
• School of Computing, Informatics, and Decision
  Systems Eng. Arizona State University
• Tempe, Arizona USA
• yau_at_asu.edu

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Evolution of Computing Paradigms
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Outline

• Trustworthy Service-Based Systems (SBS)
• Challenges of Developing Trustworthy SBS
• Human Factors in Developing Trustworthy SBS
• Current State of Art
• An Example
• Future Research

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Service-Based Systems (SBS)

• Based on service-oriented architecture (SOA)
• Adopted in various application domains
• E-business
• Health care information systems
• Homeland security
• Various collaborations
• Services in SBS provide standard interfaces for
  accessing capabilities offered by various
  providers
• Services in SBS compose to form workflows
  (business processes) to provide functionality not
  provided by any individual service

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Service-Based Systems (SBS) (cont.)

• Many advantages of SBS, among them
• Interoperation of heterogeneous systems
• Rapid composition of applications (workflows)
  from distributed services
• Adaptation to dynamic application requirements of
  users or environments (functional and QoS)

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Trustworthy SBS

• Trustworthy SBS are needed for various critical
  applications due to
• Over public or private networks, as well as
  mobile networks more open to attacks
• Interactions involving unknown entities
• Dynamic and pervasive environments
• Large-scale and cross-domain service
  collaborations
• Distributed control
• Dynamic QoS expectations for multiple workflows

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Trustworthy SBS (cont.)

• Major aspects
• Human
• Users and collaborators
• Service and infrastructure providers
• Insiders and outsiders
• Devices, software, and system architectures
• Dynamic security policies and enforcement
• Dynamic user requirements and environments
• Effective techniques
• Cost, usability and efficiency

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Trustworthy SBS (cont.)

• Various system technologies are needed for
  developing trustworthy SBS
• Security
• Trust management
• Situation awareness
• Runtime adaptation
• QoS monitoring and analysis
• QoS requirement trade-off
• Resource allocation

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Challenges of Developing Trustworthy SBS

• Interactions among services may have unforeseen
  consequences in trust, security, QoS, and risk
• Possible problems
• Untrusted/malicious services
• Intermediate results generated during service
  interactions may reveal sensitive information
• Trustworthiness of service providers,
  infrastructure providers and users

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Challenges of Developing Trustworthy SBS (cont.)

• SBS has multiple QoS requirements from multiple
  users for various applications
• Runtime tradeoffs among expected QoS requirements
• Example Mechanisms providing security protection
  are often computationally intensive and require
  certain sacrifice in other QoS (e.g. service
  delay and throughput) with available resources
• Cost, usability and efficiency

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Challenges of Developing Trustworthy SBS (cont.)

• Environments of SBS often dynamically change
• Make assessing trust and risk difficult
• Need situation awareness due to dynamic trust and
  risk
• Need adaptive enforcement of security policies
• Information needed for making decisions regarding
  trustworthiness usually distributed on multiple
  services and organizations.
• Need cooperative decision making (e.g.
  delegation, policy composition with multiple
  organizations, collaborative QoS management, risk
  assessment, trust evaluation)
• Need efficient enforcement of distributed
  security policies
• Need protection against various entities

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Challenges of Developing Trustworthy SBS (cont.)

• Service selection and composition
• How to select most appropriate services and
  compose them to satisfy both functional and QoS
  requirements of various users, while ensure
  overall system trustworthiness and security?
• Need meaningful and quantitative metrics for
  trustworthiness, security and various attributes
  of overall SBS
• How to rank service ranking to identify the
  best services satisfying their requirements

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What are Human Factors?

• In general, a human factor is a physical,
  psychological or cognitive property of an
  individual or an individual in a community, which
  is specific to humans and influences on
  technological systems as well as their
  applications.
• Examples Influences, interests, relationships
  (collaboration/competition), opinions
  (positive/negative/neutral, support/against),
  knowledge (expertise), reputation, wisdom,
  physical and psychological factors (stress,
  fatigue, fear, happy).

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Why Should Human Factors Be Considered in
Developing TSBS?

• Cyber systems become more powerful, and their
  applications become more diverse and pervasive
• Human factors are increasingly influential on the
  quality and efficiency of generating the results
  because
• SBS getting more embedded
• Applications increasingly involving multi-party
  collaborations and often more pervasive
• Applications must address multiple quality
  aspects expected by users, such as security,
  privacy, trustworthiness and performance

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Three Levels of Human Factors

• Level 1. Direct Human-and-Human Relations
• Collaboration of among users of cyber systems
• Level 2. Indirect Human-and-Human Relations
• Service-users choose the services from service
  providers through service directories
• First-time collaborations among the users based
  on past data
• Level 3. Human in Communities
• Influence among users of cyber systems
• Knowledge sharing among the users of cyber
  systems
• Matching service-users interest with services

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Direct Human-and-Human Relations

• Challenges
• How to quantify human factors in terms of the
  determinants, such as workload on the human,
  fatigue, learnability, attention, vigilance,
  human relations, human performance, human
  reliability, stress, individual differences,
  aging, safety, and results of decision making.
• How human factors affect humans themselves?

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Indirect Human and Human Relations

• Example
• In a SBS, the providers upload their
  services/applications. The users search the
  service/application directory for the SBS and
  select the services/applications they need.
  Besides the quality of the services/applications,
  each user is concerned with the trustworthiness
  of the services.
• Challenge How can a user choose a trustworthy
  service?
• Related human factors human relationships,
  stress, feedback, etc

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Human in Communities

• Challenges
• How do the human factors from one person affect
  other persons in the community?
• How do the human factors from other persons in a
  community affect one person in the community?
• How do the human factors from one person in a
  community spread in the SBS used by the
  community?

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Human in Communities (cont.)

• Example
• In a SBS, it is not easy for users to find their
  match services/applications. It is also difficult
  for the service providers to introduce their
  services/applications to possible interested
  users using the SBS.
• Challenge How to incorporate human factors to
  match all service-users with all
  services/applications automatically and
  efficiently?
• Related human factors influence, interests,
  opinions, human relationships

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Current State of Art

• Incorporating human factors in developing cyber
  systems and applications
• Research has been mainly conducted by
  researchers in psychology and sociology, and few
  computer scientists and engineers.
• Primarily focus on human-machine interactions,
  human-computer interactions, situation awareness,
  and human errors

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Current State of Art (cont.)

• Automated service composition based on various
  formal specifications
• Process calculi BPEL4WS
• QoS-aware service composition in SBS
• Optimizing QoS attributes of services using the
  genetic algorithm during the service compositions
  (G. Canfora, et al., University of Sannio,
  Italy).
• QoS provisioning for composed services, based on
  the Service Level Agreement (SLA) contracts of
  individual services (X. Gu, et al., University of
  Illinois, Urbana)
• Developing QoS-aware middleware for web service
  composition to maximize users satisfaction
  expressed in utility functions over QoS
  attributes. (L. Zeng, et al., IBM)

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Current State of Art (cont.)

• Tradeoffs among security and multiple QoS in SBS
• Development of a framework for quantifying the
  strength of system security (M. Satyanarayanan,
  et al., Carnegie Mellon University)
• An adaptive model for tradeoff between service
  performance and security in service-based
  environments (S. Yau, et al., Arizona State
  University)
• A comprehensive QoS model for service-based
  systems, (I. Jureta, et al., University of Namur,
  Belgium)

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Current State of Art (cont.)

• Adaptive resource allocation in SBS
• A multi-layered resource management framework for
  dynamic resource management in enterprise
  systems. (P. Lardieri, et al., Lockheed Martin
  Corporation)
• Decentralized online resource allocation for
  dynamic web service applications (J. Stoesser,
  et al., Universitat Karlsruhe, Germany).
• A regression based analytical model for dynamic
  resource provisioning of multi-tier applications
  (Q. Zhang, et al, HP Labs)
• Adaptive resource allocation for SBS (S. Yau, et
  al., Arizona State University)

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Current State of Art (cont.)

• Design of SBS for QoS Monitoring and adaptation
• A development methodology for adaptive
  service-based software systems (S. Yau, et al,
  Arizona State University)
• Comprehensive QoS monitoring of Web services and
  event-based SLA violation detection (Michlmayr,
  et al, Vienna University of Technology, Austria)
• Testing of SBS
• Dynamic Reconfigurable Testing of
  Service-Oriented Architecture (W. Tsai, et al,
  Arizona State University)

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Current State of Art (cont.)

• Trust estimation in SBS
• Flexible trust model for distributed service
  infrastructure (Z. Liu, University of North
  Carolina at Charlotte, S. Yau, Arizona State
  University)
• Trusted computing platforms in web services
  (Nagarajan, et al, Macquarie University,
  Australia)
• Trust management for context-aware service
  platforms (Neisse, et al, University of Twente,
  the Netherlands)
• Improving trust estimation in SBS (S. Yau and P.
  Sun, Arizona State University)

SERE 2012, S. S. Yau
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An Example Improving Trust Estimation in SBS

• Improving trust estimation in service-based
  systems by incorporating human factors as well as
  QoS profiles

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Trust Estimation in SBS

• Trust management needs to be incorporated in SBSs
  to estimate service providers trustworthiness so
  that users can decide whether to accept the
  services provided by the providers.
• Limitations of existing trust estimation
  approaches
• Only similarity of user profiles is considered
• Based on pairwise trust relationship, which
  normally does not include the transitive property
  in the propagation of trust among service
  providers.

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An Example Improving Trust Estimation in SBS
(cont.)

• Incorporating two common human factors
  competition and collaboration in interpersonal
  relationships, as well as QoS profiles, which
  have significant effects on trust estimation in
  SBS.

SERE 2012, S. S. Yau
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Network Model to be Used in Our Approach

• V a set of vertices (service providers)
• E two sets of directed edges (intention of one
  vertex to another)
• Competing edges (dashed lines)
• Collaborating edges (solid lines)
• Wedge the weighting factor of an edge, which
  indicates the weight of competition or
  collaboration.
• In the range of 0, 1

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Definition of a Transaction

• A transaction in an SBS consists of three phases
• Request phase a user of a SBS requires services.
• Selection phase the requester chooses services
  from the service providers of the SBS with trust
  values exceeding an acceptable threshold
  specified by the user.
• Feedback phase the requester gives feedback to
  the SBS on the quality of the services used.

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Definition of a Transaction (cont.)

• Information recorded in a transaction
• Which service providers selected by a service
  user
• QoS profiles of all the selected services.
• Record all aspects of quality of each service
  required by user
• Two types
• Claimed QoS profiles provided by the service
  providers along with their services when
  publishing the services.
• Feedback QoS profiles provided by the service
  user in the feedback phase after using the
  services.

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Definition of a QoS Profile

• A QoS profile of a service in a transaction is a
  vector with n elements, each of which represents
  a quantifiable aspect of the service, important
  for trust estimation.
• Consider three aspects Accuracy, delay and
  throughput
• Claimed QoS profile
• Provided by service providers
• Denoted by cProfile ltcQ1, cQ2, , cQngt
• Feedback QoS profile
• Provided by service users
• Denoted by fProfile ltfQ1, fQ2, , fQngt

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Improving Trust Estimation in SBS

• Initialization
• Initialize the trust values of all service
  providers of the SBS based on historic
  transactions using QoS profiles, collaboration
  and competition.
• Utilization
• Update the trust values of the service providers
  in current transaction using QoS profile.
• Update the trust values of all the other service
  providers using competition and collaboration.

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Improving Trust Estimation in SBS (cont.)
Trust Values (Initialization)
Existing Trust Estimation
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Trust System Adaptor
Estimated Trust Values
Original Trust Values
Trust Dissemination
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Estimated Trust Values
Estimated Trust Values
Trust refinement module calculates the trust
values of service providers based on QoS
profiles, competition and collaboration.
Trust Refinement Module
Profile evaluation module processes the
transactions to extract the QoS profiles.
Service-Based System
QoS Profiles
2
Transactions (Utilization)
1
Profile Evaluation Module
Log(s)
Log Transactions (Initialization)
1
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Improving Trust Estimation in SBS (cont.)
Trust Values (Initialization)
Existing Trust Estimation
3
Trust System Adaptor
5
6
Original Trust Values
Estimated Trust Values
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Trust Dissemination
4
Estimated Trust Values
Estimated Trust Values
Trust Refinement Module
The estimated trust values are dis-seminated to
service providers in SBS
Service-Based System
QoS Profiles
2
Transactions (Utilization)
1
Profile Evaluation Module
Log(s)
Log Transactions (Initialization)
1
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Effect of QoS Profile on Trust Estimation

• If the feedback QoS profiles of a selected
  service is better than its corresponding claimed
  QoS profiles, then the service user can decide
  the service provider is more trustworthy, and
  consequently increase the estimated trust value
  of the service provider.
• Otherwise, decrease the estimated trust value of
  the service provider.

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Comparison of Claimed and Feedback QoS Profiles

• For a given service, ith signed normalized aspect
  difference (snadi) is the signed normalized
  difference between the ith aspects of its
  feedback QoS profile and its claimed QoS profile.
• Sign of snadi is decided by the system
  administrator, e.g.
• cQi is better than fQi, positive
• cQi is worse than fQi, negative
• snadi is given by

where di_max and di_min are the maximal and
minimal differences of the ith aspect among all
transactions
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Comparison of Claimed and Feedback QoS Profiles
(cont.)

• Compare cProfile and fProfile
• M is the overall score of the comparison.
• where w1, , wn are user-assigned weights for
  various aspects in QoS profile

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Improvement of Trust Estimation Using QoS Profiles

• If M is positive, then the feedback QoS profile
  is better than the claimed QoS profile
  otherwise, the feedback QoS profile is worse than
  the claimed QoS profile.
• Improvement of the trust estimation using QoS
  profiles.
• T(u) is improved estimated trust value of
  service provider u
• T(u) is original estimated trust value of service
  provider u
• ? is the parameter for adjusting the effect of M
• By default, ? 0.85.

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Improvement of Trust Estimation Using QoS
Profiles (cont.)

• Rule 1. Competition relationship increases the
  trust values of the participants in the
  competition group.
• Competition limits free-ride
• The more time one spends, the more one is likely
  to trust the people in this group.
• Rule 2. Collaboration relationship increases
  trust.
• When two persons collaborate with each other,
  they tend to solve problems together and this
  process can help to build trust between them.
• Rule 3. Transitive property of trust.
• Whenever one service providers trust value
  changes, the trust values of his/her neighbors
  will also change accordingly.
• The trust value of a service provider is
  uniformly propagated to all the other service
  providers he intends to compete or collaborate
  with.

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Improvement of Trust Estimation Using QoS
Profiles (cont.)

• Rules 1 and 2 show the positive correlation
  between trust and competition or collaboration.
• Rule 3 defines how the trust values should be
  propagated among the whole network of SBS.
• The propagation of the trust values of service
  providers is similar to PageRank (a webpage
  reputation estimation approach).
• The more people who intend to compete or
  collaborate with a service provider, the more
  trustful the service provider is.

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Expertise Needed to Incorporate Human Factors in
Developing TSBS

• Services and cloud computing
• Software and systems engineering
• Networking, including mobile ad hoc networks,
  intelligent devices, and social networks
• Information assurance and security
• Cognitive science
• Psychology
• Business
• Culture

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Future Research to Incorporate Human Factors in
Developing TSBS

• Develop meaningful metrics to quantify human
  factors and QoS aspects of SBS, including trust,
  security and others useful for developing TSBS
• Develop a general framework with necessary
  techniques and tools to effectively incorporate a
  variety of relevant human factors in developing
  TSBS
• Validation

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• Thank you