Computer Security and Privacy Lecture Notes .ppt

Lecture 2. Information Security
Policies: General Principles

2
Outline — Information Security Policies
1) Introduction (def., dimensions, basic principles, …)
2) Recognition of the need for privacy
3) Threats to privacy
4) Privacy Controls
4.1) Technical privacy controls - Privacy-Enhancing Technologies
(PETs)
a) Protecting user identities
b) Protecting usee identities
c) Protecting confidentiality & integrity of personal data
4.2) Legal privacy controls
a) Legal World Views on Privacy
b) International Privacy Laws: Comprehensive or Sectoral
c) Privacy Law Conflict between European Union – USA
d) A Common Approach: Privacy Impact Assessments (PIA)
e) Observations & Conclusions
5) Selected Advanced Topics in Privacy
5.1) Privacy in pervasive computing
5.2) Using trust paradigm for privacy protection
5.3) Privacy metrics
5.4) Trading privacy for trust

3
1. Introduction
• POLICY: “A plan or course of action that
influences decisions”
• Policy is the essential foundation of an
effective information security program
–“The success of an information resources protection
program depends on the policy generated, and on the
attitude of management toward securing information
on automated systems”
• Policy maker sets the tone and emphasis on
the importance of information security

4
1. Introduction (2)
 The basic purposes of policy are that it
should:
 Protect people and information
 Set the rules for expected behaviour by users, system
administrator, management, and security personnel
 Authorize security personnel to monitor, probe, and
investigate
 Define and authorize the consequences of violation
 Define the company consensus baseline stance on security
 Help minimize risk
 Help track compliance with regulations and legislation

5
2. Recognition of Need for Privacy
Guarantees (1)
 By individuals [Cran et al. ‘99]

99% unwilling to reveal their SSN
 18% unwilling to reveal their… favorite TV show
 By businesses
 Online consumers worrying about revealing personal
data
held back $15 billion in online revenue in 2001
 By Federal government
 Privacy Act of 1974 for Federal agencies
 Health Insurance Portability and Accountability Act of
1996 (HIPAA)

6
 By computer industry research (examples)
 Microsoft Research

The biggest research challenges:
According to Dr. Rick Rashid, Senior Vice President for Research
 Reliability / Security / Privacy / Business Integrity

Broader: application integrity (just “integrity?”)
=> MS Trustworthy Computing Initiative

Topics include: DRM—digital rights management (incl.
watermarking surviving photo editing attacks), software rights
protection, intellectual property and content protection, database
privacy and p.-p. data mining, anonymous e-cash, anti-spyware
 IBM (incl. Privacy Research Institute)

Topics include: pseudonymity for e-commerce, EPA and EPAL—
enterprise privacy architecture and language, RFID privacy, p.-p.
video surveillance, federated identity management (for enterprise
federations), p.-p. data mining and p.-p.mining of association rules,
hippocratic (p.-p.) databases, online privacy monitoring
2. Recognition of Need for Privacy Guarantees (2)

7
 By academic researchers (examples from the U.S.A.)

CMU and Privacy Technology Center

Latanya Sweeney (k-anonymity, SOS—Surveillance of Surveillances,
genomic privacy)

Mike Reiter (Crowds – anonymity)

Purdue University – CS and CERIAS

Elisa Bertino (trust negotiation languages and privacy)

Bharat Bhargava (privacy-trust tradeoff, privacy metrics, p.-p. data
dissemination, p.-p. location-based routing and services in networks)

Chris Clifton (p.-p. data mining)

Leszek Lilien (p.-p. data disemination)

UIUC

Roy Campbell (Mist – preserving location privacy in pervasive computing)

Marianne Winslett (trust negotiation w/ controled release of private
credentials)

U. of North Carolina Charlotte

Xintao Wu, Yongge Wang, Yuliang Zheng (p.-p. database testing and data
mining)
2. Recognition of Need for Privacy Guarantees (3)

8
3. Threats to Privacy (1) [cf. Simone Fischer-Hübner]
1) Threats to privacy at application level
 Threats to collection / transmission of large quantities
of personal data
 Incl. projects for new applications on Information Highway, e.g.:

Health Networks / Public administration Networks

Research Networks / Electronic Commerce / Teleworking

Distance Learning / Private use
 Example: Information infrastructure for a better healthcare
[cf. Danish "INFO-Society 2000"- or Bangemann-Report]

National and European healthcare networks for the interchange of
information

Interchange of (standardized) electronic patient case files

Systems for tele-diagnosing and clinical treatment

9
3. Threat to Privacy (2) [cf. Simone Fischer-Hübner]
2) Threats to privacy at communication level
 Threats to anonymity of sender / forwarder /
receiver
 Threats to anonymity of service provider
 Threats to privacy of communication
 E.g., via monitoring / logging of transactional data

Extraction of user profiles & its long-term storage
3) Threats to privacy at system level
 E.g., threats at system access level
4) Threats to privacy in audit trails

10
3. Threat to Privacy (3) [cf. Simone Fischer-Hübner]
 Identity theft – the most serious crime against privacy
 Threats to privacy – another view
 Aggregation and data mining
 Poor system security
 Government threats

Gov’t has a lot of people’s most private data
 Taxes / homeland security / etc.

People’s privacy vs. homeland security concerns
 The Internet as privacy threat

Unencrypted e-mail / web surfing / attacks
 Corporate rights and private business

Companies may collect data that U.S. gov’t is not allowed to
 Privacy for sale - many traps

“Free” is not free…
 E.g., accepting frequent-buyer cards reduces your
privacy

11
4. PrivacyControls
1) Technical privacy controls - Privacy-Enhancing
Technologies (PETs)
a) Protecting user identities
b) Protecting usee identities
c) Protecting confidentiality & integrity of personal data
2) Legal privacy controls

12
4.1. Technical PrivacyControls (1)
 Technical controls - Privacy-Enhancing Technologies
(PETs)
[cf. Simone Fischer-Hübner]
a) Protecting user identities via, e.g.:
 Anonymity - a user may use a resource or service
without disclosing her identity
 Pseudonymity - a user acting under a pseudonym
may use a resource or service without disclosing his
identity
 Unobservability - a user may use a resource or
service without others being able to observe that the
resource or service is being used
 Unlinkability - sender and recipient cannot be
identified as communicating with each other

13
4.1. Technical Privacy Controls (2)

Taxonomies of pseudonyms [cf. Simone Fischer-
Hübner]
 Taxonomy of pseudonyms w.r.t. their function
i) Personal pseudonyms

Public personal pseudonyms / Nonpublic personal
pseudonyms / Private personal pseudonyms
ii) Role pseudonyms

Business pseudonyms / Transaction pseudonyms
 Taxonomy of pseudonyms w.r.t. their generation
i) Self-generated pseudonyms
ii) Reference pseudonyms
iii) Cryptographic pseudonyms
iv) One-way pseudonyms

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b) Protecting usee identities via, e.g.: [cf. Simone Fischer-
Hübner]
Depersonalization (anonymization) of data subjects
 Perfect depersonalization:

Data rendered anonymous in such a way that the data
subject is no longer identifiable
 Practical depersonalization:

The modification of personal data so that the information
concerning personal or material circumstances can no
longer or only with a disproportionate amount of time,
expense and labor be attributed to an identified or
identifiable individual
 Controls for depersonalization include:

Inference controls for statistical databases

Privacy-preserving methods for data mining

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 The risk of reidentification (a threat to anonymity)

Types of data in statistical records:
 Identity data - e.g., name, address, personal number
 Demographic data - e.g., sex, age, nationality
 Analysis data - e.g., diseases, habits

The degree of anonymity of statistical data depends on:
 Database size
 The entropy of the demographic data attributes that can serve
as supplementary knowledge for an attacker

The entropy of the demographic data attributes depends
on:
 The number of attributes
 The number of possible values of each attribute
 Frequency distribution of the values
 Dependencies between attributes

16
c) Protecting confidentiality and integrity of personal data
via, e.g.:
 Privacy-enhanced identity management
 Limiting access control

Incl. formal privacy models for access control
 Enterprise privacy policies
 Steganography
 Specific tools

Incl. P3P (Platform for Privacy Preferences)

17
4.2. Legal PrivacyControls (1)
 Outline
a) Legal World Views on Privacy
b) International Privacy Laws:

Comprehensive Privacy Laws

Sectoral Privacy Laws
c) Privacy Law Conflict European Union vs. USA
d) A Common Approach: Privacy Impact Assessments
(PIA)
e) Observations & Conclusions

18
4.2. Legal Privacy Controls (2)
a) Legal World Views on Privacy (1)
 General belief: Privacy is a fundamental human
right that has become one of the most important
rights of the modern age
 Privacy also recognized and protected by
individual countries
 At a minimum each country has a provision for rights of
inviolability of the home and secrecy of communications
 Definitions of privacy vary according to context and
environment
[cf. A.M. Green, Yale, 2004]

19
a) Legal World Views on Privacy (2)
United States: “Privacy is the right to be left alone” -
Justice Louis Brandeis
UK: “the right of an individual to be protected against
intrusion into his personal life or affairs by direct
physical means or by publication of information
Australia: “Privacy is a basic human right and the
reasonable expectation of every person”
[A.M. Green, Yale, 2004]

20
b) International Privacy Laws
 Two types of privacy laws in various countries:
1) Comprehensive Laws
 Def: General laws that govern the collection, use and
dissemination of personal information by public & private
sectors
 Require commissioners or independent enforcement body
 Difficulty: lack of resources for oversight and enforcement;
agencies under government control
 Examples: European Union, Australia, Canada and the UK
2) Sectoral Laws
 Idea: Avoid general laws, focus on specific sectors instead
 Advantage: enforcement through a range of mechanisms
 Disadvantage: each new technology requires new legislation
 Example: United States

21
4.2. Legal Privacy Controls (5) -- b) International Privacy Laws
Comprehensive Laws - European Union
 European Union Council adopted the new Privacy
Electronic Communications Directive [cf. A.M. Green, Yale,
2004]
 Prohibits secondary uses of data without informed consent
 No transfer of data to non EU countries unless there is
adequate privacy protection

Consequences for the USA
 EU laws related to privacy include
 1994 — EU Data Protection Act
 1998 — EU Data Protection Act

Privacy protections stronger than in the U.S.

22
Sectoral Laws - United States (1)
 No explicit right to privacy in the constitution
 Limited constitutional right to privacy implied in
number of provisions in the Bill of Rights
 A patchwork of federal laws for specific categories of
personal information
 E.g., financial reports, credit reports, video rentals, etc.
 No legal protections, e.g., for individual’s privacy on
the internet are in place (as of Oct. 2003)
 White House and private sector believe that self-
regulation is enough and that no new laws are
needed (exception: medical records)
 Leads to conflicts with other countries’ privacy policies

23
Sectoral Laws - United States (2)
 American laws related to privacy include:
 1974 — US Privacy Act

Protects privacy of data collected by the executive branch
of federal gov’t
 1984 — US Computer Fraud and Abuse Act

Penalties: max{100K, stolen value} and/or 1 to 20 yrs
 1986 — US Electronic Communications Privacy Act

Protects against wiretapping

Exceptions: court order, ISPs
 1996 — US Economic Espionage Act
 1996 — HIPAA

Privacy of individuals’ medical records
 1999 — Gramm-Leach-Bliley Act

Privacy of data for customers of financial institutions
 2001 — USA Patriot Act
 — US Electronic Funds Transfer Act
 — US Freedom of Information Act

24
c) Privacy Law Conflict: EU vs. The United
States
 US lobbied EU for 2 years (1998-2000) to convince it that
the US system is adequate
 Result was the “Safe Harbor Agreement” (July 2000):
US companies would voluntarily self-certify to adhere
to a set of privacy principles worked out by US
Department of Commerce and Internal Market
Directorate of the European Commission
 Little enforcement: A self-regulatory system in which
companies merely promise not to violate their declared
privacy practices
 Criticized by privacy advocates and consumer groups in both
US and Europe
 Agreement re-evaluated in 2003
 Main issue: European Commission doubted effectiveness of
the sectoral/self-regulatory approach

25
d) A Common Approach:
Privacy Impact Assessments (PIA) (1)
 An evaluation conducted to assess how the adoption
of new information policies, the procurement of new
computer systems, or the initiation of new data
collection programs will affect individual privacy
 The premise: Considering privacy issues at the early
stages of a project cycle will reduce potential adverse
impacts on privacy after it has been implemented
 Requirements:
 PIA process should be independent
 PIA performed by an independent entity (office and/or
commissioner) not linked to the project under review
 Participating countries: US, EU, Canada, etc.

26
d) A Common Approach: PIA (2)
 EU implemented PIAs
 Under the European Union Data Protection Directive, all
EU members must have an independent privacy
enforcement body
 PIAs soon to come to the United States (as of 2003)
 US passed the E-Government Act of 2002 which
requires federal agencies to conduct privacy impact
assessments before developing or procuring
information technology

27
e) Observations and Conclusions
 Observation 1: At present too many mechanisms seem to
operate on a national or regional, rather than global level
 E.g., by OECD
 Observation 2: Use of self-regulatory mechanisms for the
protection of online activities seems somewhat haphazard
and is concentrated in a few member countries
 Observation 3: Technological solutions to protect privacy
are implemented to a limited extent only
 Observation 4: Not enough being done to encourage the
implementation of technical solutions for privacy
compliance and enforcement
 Only a few member countries reported much activity in this area

28
e) Observations and Conclusions
 Conclusions
 Still work to be done to ensure the security of personal
information for all individuals in all countries
 Critical that privacy protection be viewed in a global
perspective

Better than a purely national one –
To better handle privacy violations that cross national borders

29
5. Selected Advanced Topics in Privacy (1)
Outline
5.1) Privacy in pervasive computing
5.2) Using trust paradigm for privacy protection
5.3) Privacy metrics
5.4) Trading privacy for trust

30
5. Selected Advanced Topics in Privacy
5.1. Privacy in Pervasive Computing (1)
 In pervasive computing environments, socially-based
paradigms (incl. trust) will play a big role
 People surrounded by zillions of computing devices of all
kinds, sizes, and aptitudes [“Sensor Nation: Special Report,” IEEE Spectrum, vol. 41, no. 7, 2004 ]

Most with limited / rudimentary capabilities

Quite small, e.g., RFID tags, smart dust

Most embedded in artifacts for everyday use, or even human bodies

Possible both beneficial and detrimental (even apocalyptic) consequences
 Danger of malevolent opportunistic sensor networks
— pervasive devices self-organizing into huge spy networks

Able to spy anywhere, anytime, on everybody and everything

Need means of detection & neutralization

To tell which and how many snoops are active, what data they collect,
and who they work for

An advertiser? a nosy neighbor? Big Brother?

Questions such as “Can I trust my refrigerator?” will not be jokes

The refrigerator snitching on its owner’s dietary misbehavior for her doctor

31
5.1. Privacy in Pervasive Computing (2)
 Will pervasive computing destroy privacy? (as we know it)
 Will a cyberfly end privacy?

With high-resolution camera eyes and supersensitive microphone ears
 If a cyberfly too clever drown in the soup, we’ll build cyberspiders
 But then opponents’ cyberbirds might eat those up

So, we’ll build a cybercat

And so on and so forth …
 Radically changed reality demands new approaches to
privacy
 Maybe need a new privacy category—namely, artifact privacy?
 Our belief: Socially based paradigms (such as trust-based approaches) will
play a big role in pervasive computing

Solutions will vary (as in social settings)
 Heavyweighty solutions for entities of high intelligence and capabilities (such
as humans and intelligent systems) interacting in complex and important matters

Lightweight solutions for less intelligent and capable entities interacting in
simpler matters of lesser consequence

32
5.2. Using Trust for Privacy Protection (1)
 Privacy = entity’s ability to control the availability and
exposure of information about itself
 We extended the subject of privacy from a person in the original
definition [“Internet Security Glossary,” The Internet Society, Aug.
2004 ] to an entity— including an organization or software

Controversial but stimulating

Important in pervasive computing
 Privacy and trust are closely related
 Trust is a socially-based paradigm
 Privacy-trust tradeoff: Entity can trade privacy for a
corresponding gain in its partners’ trust in it
 The scope of an entity’s privacy disclosure should be proportional
to the benefits expected from the interaction

As in social interactions

E.g.: a customer applying for a mortgage must reveal much
more personal data than someone buying a book

33
 Optimize degree of privacy traded to gain trust

Disclose minimum needed for gaining partner’s necessary trust
level
 To optimize, need privacy & trust measures
Once measures available:

Automate evaluations of the privacy loss and trust gain
 Quantify the trade-off
 Optimize it
 Privacy-for-trust trading requires privacy guarantees for
further dissemination of private info

Disclosing party needs satisfactory limitations on further
dissemination (or the lack of thereof) of traded private information

E.g., needs partner’s solid privacy policies

Merely perceived danger of a partner’s privacy violation can make the
disclosing party reluctant to enter into a partnership
 E.g., a user who learns that an ISP has carelessly revealed any customer’s
email will look for another ISP

34
 Conclusions on Privacy and Trust
 Without privacy guarantees, there can be no trust and trusted
interactions

People will avoid trust-building negotiations if their privacy is
threatened by the negotiations

W/o trust-building negotiations no trust can be established

W/o trust, there are no trusted interactions
 Without privacy guarantees, lack of trust will cripple the
promise of pervasive computing

Bec. people will avoid untrusted interactions with privacy-invading
pervasive devices / systems
 E.g., due to the fear of opportunistic sensor networks
Self-organized by electronic devices around us – can harm people in
their midst
 Privacy must be guaranteed for trust-building negotiations

35
5.3. Privacy Metrics (1)
Outline
 Problem and Challenges
 Requirements for Privacy Metrics
 Related Work
 Proposed Metrics
A. Anonymity set size metrics
B. Entropy-based metrics

36
a) Problem and Challenges
 Problem
 How to determine that certain degree of data
privacy is provided?
 Challenges
 Different privacy-preserving techniques or
systems claim different degrees of data
privacy
 Metrics are usually ad hoc and customized

Customized for a user model

Customized for a specific technique/system

Need to develop uniform privacy metrics

To confidently compare different techniques/systems

37
b) Requirements for Privacy
Metrics
 Privacy metrics should account for:
 Dynamics of legitimate users

How users interact with the system?
E.g., repeated patterns of accessing the same data
can leak information to a violator
 Dynamics of violators

How much information a violator gains by watching
the system for a period of time?
 Associated costs

Storage, injected traffic, consumed CPU cycles,
delay

38
5.3. Privacy Metrics (3b)
c) Related Work
 Anonymity set without accounting for probability
distribution [Reiter and Rubin, 1999]
 An entropy metric to quantify privacy level,
assuming static attacker model [Diaz et al., 2002]
 Differential entropy to measure how well an
attacker estimates an attribute value [Agrawal
and Aggarwal 2001]

39
d) Proposed Metrics
A. Anonymity set size metrics
B. Entropy-based metrics

40
A. Anonymity Set Size Metrics
 The larger set of indistinguishable entities, the
lower probability of identifying any one of them
 Can use to ”anonymize” a selected private attribute
value within the domain of its all possible values
“Hiding in a crowd”
“More” anonymous (1/n)
“Less” anonymous (1/4)

41
Anonymity Set
 Anonymity set A
A = {(s1, p1), (s2, p2), …, (sn, pn)}
 si: subject i who might access private data
or: i-th possible value for a private data attribute
 pi: probability that si accessed private data
or: probability that the attribute assumes the i-th possible
value

42
Effective Anonymity Set Size
 Effective anonymity set size is
 Maximum value of L is |A| iff all pi’’s are equal to 1/|
A|
 L below maximum when distribution is skewed
 skewed when pi’’s have different values
 Deficiency:
L does not consider violator’s learning behavior



|
|
1
|)
|
/
1
,
min(
|
|
A
i
i A
p
A
L

43
B. Entropy-based Metrics
 Entropy measures the randomness, or
uncertainty, in private data
 When a violator gains more information,
entropy decreases
 Metric: Compare the current entropy
value with its maximum value
 The difference shows how much information
has been leaked

44
Dynamics of Entropy
 Decrease of system entropy with attribute
disclosures (capturing dynamics)

When entropy reaches a threshold (b), data evaporation can be invoked to
increase entropy by controlled data distortions

When entropy drops to a very low level (c), apoptosis can be triggered to
destroy private data

Entropy increases (d) if the set of attributes grows or the disclosed
attributes become less valuable – e.g., obsolete or more data now available
(a) (b) (c) (d)
Disclosed
attributes
H*
All
attribute
s
Entrop
y
Level

45
Quantifying Privacy Loss
 Privacy loss D(A,t) at time t, when a subset of attribute
values A might have been disclosed:
 H*
(A) – the maximum entropy
 Computed when probability distribution of pi’s is uniform
 H(A,t) is entropy at time t
 wj – weights capturing relative privacy “value” of
attributes
)
,
(
)
(
)
,
( *
t
A
H
A
H
t
A
D 

   
 
 
 










|
|
1
2
log
,
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i
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p
w
t
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46
Using Entropy in Data Dissemination
 Specify two thresholds for D
 For triggering evaporation
 For triggering apoptosis
 When private data is exchanged
 Entropy is recomputed and compared to the
thresholds
 Evaporation or apoptosis may be invoked to
enforce privacy

47
Entropy: Example
 Consider a private phone number: (a1a2a3) a4a5 a6 – a7a8a9 a10
 Each digit is stored as a value of a separate attribute
 Assume:
 Range of values for each attribute is [0—9]
 All attributes are equally important, i.e., wj = 1
 The maximum entropy – when violator has no information
about the value of each attribute:
 Violator assigns a uniform probability distribution to
values of each attribute
 e.g., a1= i with probability of 0.10 for each i in [0—9]
 
 
 
 










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9
0
10
1
2
*
3
.
33
1
.
0
log
1
.
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(
j i
j
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A
H

48
Entropy: Example – cont.
 Suppose that after time t, violator can figure out the state of the
phone number, which may allow him to learn the three leftmost
digits
 Entropy at time t is given by:
 Attributes a1, a2, a3 contribute 0 to the entropy value because violator
knows their correct values
 Information loss at time t is:
   
 
 
 











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4
9
0
2
3
.
23
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.
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49
Selected Publications
 “Private and Trusted Interactions,” by B. Bhargava and L. Lilien.
 “On Security Study of Two Distance Vector Routing Protocols for Mobile Ad Hoc
Networks,” by W. Wang, Y. Lu and B. Bhargava, Proc. of IEEE Intl. Conf. on Pervasive
Computing and Communications (PerCom 2003), Dallas-Fort Worth, TX, March 2003.
http://www.cs.purdue.edu/homes/wangwc/PerCom03wangwc.pdf
 “Fraud Formalization and Detection,” by B. Bhargava, Y. Zhong and Y. Lu, Proc. of 5th Intl.
Conf. on Data Warehousing and Knowledge Discovery (DaWaK 2003), Prague, Czech
Republic, September 2003. http://www.cs.purdue.edu/homes/zhong/papers/fraud.pdf
 “Trust, Privacy, and Security. Summary of a Workshop Breakout Session at the National
Science Foundation Information and Data Management (IDM) Workshop held in Seattle,
Washington, September 14 - 16, 2003” by B. Bhargava, C. Farkas, L. Lilien and F.
Makedon, CERIAS Tech Report 2003-34, CERIAS, Purdue University, November 2003.
http://www2.cs.washington.edu/nsf2003 or
https://www.cerias.purdue.edu/tools_and_resources/bibtex_archive/archive/2003-34.pdf
 “e-Notebook Middleware for Accountability and Reputation Based Trust in Distributed
Data Sharing Communities,” by P. Ruth, D. Xu, B. Bhargava and F. Regnier, Proc. of the
Second International Conference on Trust Management (iTrust 2004), Oxford, UK, March
2004. http://www.cs.purdue.edu/homes/dxu/pubs/iTrust04.pdf
 “Position-Based Receiver-Contention Private Communication in Wireless Ad Hoc
Networks,” by X. Wu and B. Bhargava, submitted to the Tenth Annual Intl. Conf. on
Mobile Computing and Networking (MobiCom’04), Philadelphia, PA, September - October
2004.
http://www.cs.purdue.edu/homes/wu/HTML/research.html/paper_purdue/mobi04.pdf

50
Introduction to Privacy in Computing
References & Bibliography (1)
Ashley Michele Green, “International Privacy Laws. Sensitive
Information in a Wired World,” CS 457 Report, Dept. of Computer
Science, Yale Univ., October 30, 2003.
Simone Fischer-Hübner,
"IT-Security and Privacy-Design and Use of Privacy-Enhancing Sec
urity Mechanisms", Springer Scientific Publishers, Lecture Notes
of Computer Science, LNCS 1958
, May 2001, ISBN 3-540-42142-4.
Simone Fischer-Hübner, “
Privacy Enhancing Technologies, PhD course,” Session 1 and 2,
Department of Computer Science, Karlstad University,
Winter/Spring 2003,
[available at: http://www.cs.kau.se/~simone/kau-phd-
course.htm].

51
Introduction to Privacy in Computing
References & Bibliography (2)
 Slides based on BB+LL part of the paper:
Bharat Bhargava, Leszek Lilien, Arnon Rosenthal, Marianne Winslett,
“Pervasive Trust,” IEEE Intelligent Systems, Sept./Oct. 2004, pp.74-77
 Paper References:
1. The American Heritage Dictionary of the English Language, 4th ed., Houghton Mifflin, 2000.
2. B. Bhargava et al., Trust, Privacy, and Security: Summary of a Workshop Breakout Session at the National
Science Foundation Information and Data Management (IDM) Workshop held in Seattle,Washington, Sep.
14–16, 2003, tech. report 2003-34, Center for Education and Research in Information Assurance and
Security, Purdue Univ., Dec. 2003;
www.cerias.purdue.edu/tools_and_resources/bibtex_archive/archive/2003-34.pdf.
3. “Internet Security Glossary,” The Internet Society, Aug. 2004; www.faqs.org/rfcs/rfc2828.html.
4. B. Bhargava and L. Lilien “Private and Trusted Collaborations,” to appear in Secure Knowledge
Management (SKM 2004): A Workshop, 2004.
5. “Sensor Nation: Special Report,” IEEE Spectrum, vol. 41, no. 7, 2004.
6. R. Khare and A. Rifkin, “Trust Management on the World Wide Web,” First Monday, vol. 3, no. 6, 1998;
www.firstmonday.dk/issues/issue3_6/khare.
7. M. Richardson, R. Agrawal, and P. Domingos,“Trust Management for the Semantic Web,” Proc. 2nd
Int’l Semantic Web Conf., LNCS 2870, Springer-Verlag, 2003, pp. 351–368.
8. P. Schiegg et al., “Supply Chain Management Systems—A Survey of the State of the Art,”
Collaborative Systems for Production Management: Proc. 8th Int’l Conf. Advances in Production
Management Systems (APMS 2002), IFIP Conf. Proc. 257, Kluwer, 2002.
9. N.C. Romano Jr. and J. Fjermestad, “Electronic Commerce Customer Relationship Management: A
Research Agenda,” Information Technology and Management, vol. 4, nos. 2–3, 2003, pp. 233–258.

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