This document discusses privacy concerns arising from clashes between new technological capabilities and societal expectations of privacy. It summarizes issues such as statistical databases allowing individual inference, data aggregation uniquely identifying individuals, monitoring technologies for location, behavior and communications, and challenges around encryption, data repurposing, digital watermarking, implanted RFIDs, genome sequencing, and more. The conclusion notes that many new technologies violate privacy expectations, and that laws have lagged in addressing these issues created by advancing technologies.

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Privacy Concerns: The Clash
between Technological Capabilities
and Societal Expectations
Ernst L. Leiss
Department of Computer Science
University of Houston
coscel@cs.uh.edu
Partially funded under NSF Grant #1241772
Any opinions, findings, conclusions, or recommendations expressed herein are those
of the author and do not reflect the views of the National Science Foundation
Updated 28 Feb. 2018

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
1. Introduction: Societal Expectations
2. Statistical databases: Inference control
3. Data aggregation
4. Monitoring the location of an individual: Cell phones, tracking devices for vehicles,
devices that read license plates of vehicles and monitor their location,
face recognition software for public monitoring cameras
5. Monitoring the behavior of an individual: Cars with devices that record speed,
acceleration and deceleration, g-forces (in turns), recording websites visited
6. Monitoring communications: Cell phones vs. landlines (US), monitoring under a court
order, monitoring international communications (Echelon)
7. Smartphones
8. Encryption: Access to encrypted communication under a court order, requiring the
divulgence of keys and passwords (self-incrimination), treating encryption algorithms as
munitions (US) or restricting/forbidding its use
9. SMS, texting, etc.: For business purposes, the lack of a central storage and monitoring
unit may be undesirable
10. Repurposing data sets collected for a different purpose
11. The use of watermarking to trace digital content
12. Implanted RFIDs for people?
13. The use of an individual’s fully sequenced genome
14. Conclusion

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
1. Introduction: Societal Expectations
Behavior: Law, ethics
For individuals, organizations, and government
Laws should reflect ethics
Technology: Unexpected and unknown capabilities
May clash with a society’s expectations and ethics
The expectation of privacy: may be contradicted by new technological capabilities
Legal landscape: US vs. EU
This talk is concerned with the tension between what we can do and what we
should do

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
2. Statistical databases: Inference control
The notion of a statistical database
Access to individual entries vs. access to statistics
Confidential data, legal requirements to maintain confidentiality:
The use of statistical queries must guarantee confidentiality
In practice, extremely difficult to achieve
Extensive literature, for many approaches, restrictions, and models of
statistical databases

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
2. Statistical databases: Inference control
Example
Assume ordinary SQL queries
Each query involves a set of elements
Typical: Median, average, sum
Confidential information in statistical queries is numerical
Assume query type SUM: A query defines a set of elements and returns the
sum of the confidential information associated with the elements in the set
Can do or (union of underlying set) and not (complement of set)
Restriction: A query q is legal iff h <= NU(q) <= N-h
N is the total number of elements in the database, h is an arbitrary value,
NU(q) is the number of elements in the set underlying the query q
General tracker: Any query GT s. t. 2h <= NU(GT) <= N-2h
Illegal query qbad:
x := SUM(GT) + SUM(notGT)
SUM(qbad) = SUM(qbad or GT) + SUM(qbad or not GT) – x too small
SUM(qbad) = 2x – [SUM(not qbad or GT) + SUM(not qbad or not GT)] too large

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
2. Statistical databases: Inference control
Upshot
Impossible to maintain the confidentiality of the information of
individual entries
Similar results hold for virtually all type of restrictions
that are imposed on the queries
Randomizing is a possible solution, except the responses
are falsified (slightly)

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
3. Data aggregation
Two entirely innocuous databases may be combined to
identify uniquely an individual
Example:
US: DoB; 5-digit zip code
330M; 100 000 zip codes: on average 3 300 inhabitants per zip
365 x 80 birth dates: on average 12 000 people with same DoB
Combined: DoB plus zip code identifies uniquely many, if not most
individuals: 100 000 x 365 x 80 >> 330M

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
4. Monitoring the location of an individual
Cell phones: Store and transmit location information, service providers may
provide this information
Tracking devices for vehicles: Can be used to locate individuals, may be
attached without the owner’s knowledge
Devices that read license plates of vehicles and monitor their location
Law enforcement, traffic restrictions (Inner City), toll booths
Face recognition software for public monitoring cameras
Ownership of data collected

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
5. Monitoring the behavior of an individual
Cars with devices that record driving behavior
speed, acceleration and deceleration, g-forces (in turns)
Recording websites visited
websites, time, duration
Point-of-Sales information
acquisition of items, matching credit card information with purchases
Ownership of data collected

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
6. Monitoring communications
Cell phones vs. landlines (US)
“Americans’ Cellphones Targeted in Secret U.S. Spy Program”
(Nov 14, 2014, WSJ) Devices on planes mimic cellphone towers to target criminals’
phones but also gather information from thousands of other phones
Ownership of communication systems: Employers monitoring
employees’ e-mail and surfing/downloading behavior
Monitoring under a court order
Monitoring international communications (Echelon)

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
7. Smartphones
The danger of sensors, the abuse of apps
Motion detector app: can be used to infer “keystrokes”, e. g. to guess PINs
Gyroscope, accelerometer, light sensor, magnetism measuring
magnetometer, microphone (tapping noise)
Apps may be “overloaded” – activate one function and the camera or the
microphone are also activated

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
8. Encryption
Encryption is dual-use: Civilian and government (law-
enforcement, military)
Access to encrypted communication under a court order
Requiring the divulgence of keys and passwords (self-
incrimination)
Treating encryption algorithms as munitions (US) or
restricting/forbidding its use

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
9. SMS, texting, etc.
Ubiquitous technology
Private use
Business use: lack of central storage and monitoring unit undesirable
Lack of control

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
10. Repurposing Data Sets
In much scientific research, data sets are collected.
Permissions to use such data sets must often be secured.
These permissions often define specific purposes of study and analysis.
It is frequently tempting to reuse such a data set for a different study or
analysis.
Different data sets may also be combined with the same study or analysis
objective.
It is necessary to obtain permissions for the new studies or analyses?
EU’s privacy directive vs. US sectoral legislation (related to medical, financial,
or student data)
Example: Human subjects. IRBs. Medical data sets.

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
11. Watermarking to trace digital content
Digital watermarks
Tag digital objects imperceptibly (to the human senses)
Video and audio
Can be used to identify uniquely copies of digital objects
Precedent: The Oscars

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
12. Implanted RFIDs for people?
Exist already for pets
Easily applied to people (e. g., children)
Monitoring requires extensive infrastructure (software
dependent)
Orwellian society

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
13. The use of an individual’s fully sequenced
genome
An individual’s fully sequenced genome costs now under
US$1000 (2014)
Drop in cost largely due to more efficient software (and
economy of scale)
Is it desirable for many people to know their full genome?
to predict disease susceptibility [Huntingdon’s – no cure!]
for genetic screening, e. g., for jobs and for insurance purposes
for fetal testing, including to determine termination of pregnancies
for genetic counseling

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
Use of DNA in Finding the Golden State Serial Killer (Joseph DeAngelo)
2017/8
Matches with ancestors (DeAngelo’s great-great-great grand parents!)
Huntington’s chorea: England's Court of Appeal 2017: Physicians treating patients
with it have a duty to disclose this diagnosis to the patient’s children
What about genetic testing results?
Genetic snooping: Get someone’s DNA from a discarded cup or band-aid
or tissue

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Leiss Privacy Concerns: Technological Capabilities and Societal Expectations
14. Conclusion
Raised issues of clashes between technological capabilities
and ethics
Most violate our expectation of privacy, but not all (criminals
encrypting their communications)
The problem of quality of information (e. g., toll roads vs. DMV info)
The law lags seriously behind the technology
As computer scientists, we have a greater responsibility than
ordinary citizens because we help create the technology