Signal Processing and Data Privacy

Literature Review – Talk
By Kato Mivule
COSC891 Fall 2013

Signal Processing and Machi...
Signal Processing and Data Privacy

Agenda
•
•
•
•
•
•

Introduction.
Privacy Definition Challenge.
Differential Privacy D...
Signal Processing and Data Privacy

Introduction: Privacy Preserving Data Mining
• Maintaining the privacy of individuals ...
Signal Processing and Data Privacy

Introduction: Privacy Definition
• Privacy definition is problematic.
• There are diff...
Signal Processing and Data Privacy

Differential Privacy Definition
• Cryptographically Motivated.
• Proposed by Cynthia D...
Signal Processing and Data Privacy

Differential Privacy Definition:
ε-differential privacy is satisfied if the results t...
Signal Processing and Data Privacy

Differential Privacy Definition – Types
• Query-based Differential Privacy.
• Input pe...
Signal Processing and Data Privacy

Differential Privacy – Input and Output Perturbation

Image Source: Sarwate, A.D.; Cha...
Signal Processing and Data Privacy

Differential Privacy Challenges
• There is a tension between data privacy and data uti...
Signal Processing and Data Privacy

Differential Privacy Challenges – Utility Quantification
•

Mean Squared Error – for s...
Signal Processing and Data Privacy

Differential Privacy Challenges – Limitations
• Time Series and Filtering problems.
• ...
Signal Processing and Data Privacy

Differential Privacy – Signal Processing Applications
•

Apply differential piracy in ...
Signal Processing and Data Privacy

Differential Privacy – Applications
• Integrate differential privacy in signal process...
Signal Processing and Data Privacy

Conclusion
•

A general overview of the differential privacy is given.

•

The paper f...
Signal Processing and Data Privacy

References
•

Sarwate, A.D.; Chaudhuri, K., "Signal Processing and Machine Learning wi...
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Lit Review Talk - Signal Processing and Machine Learning with Differential Privacy Algorithms and challenges for continuous data

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Literature Review – Talk, By Kato Mivule, COSC891 Fall 2013, Computer Science Department, Bowie State University
"Signal Processing and Machine Learning with Differential Privacy Algorithms and challenges for continuous data" Sarwate and Chaudhuri (2013)

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Lit Review Talk - Signal Processing and Machine Learning with Differential Privacy Algorithms and challenges for continuous data

  1. 1. Signal Processing and Data Privacy Literature Review – Talk By Kato Mivule COSC891 Fall 2013 Signal Processing and Machine Learning with Differential Privacy Algorithms and challenges for continuous data Sarwate, A.D.; Chaudhuri, K., "Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data," Signal Processing Magazine, IEEE , vol.30, no.5, pp.86,94, Sept. 2013 doi: 10.1109/MSP.2013.2259911 Bowie State University Department of Computer Science
  2. 2. Signal Processing and Data Privacy Agenda • • • • • • Introduction. Privacy Definition Challenge. Differential Privacy Definition. Differential Privacy Challenges. Differential Privacy Applications in Signal Processing. Conclusion. Bowie State University Department of Computer Science
  3. 3. Signal Processing and Data Privacy Introduction: Privacy Preserving Data Mining • Maintaining the privacy of individuals is imperative. • Individuals expect their data to be kept private despite willingness to share such info. • The Challenge is how extract knowledge from large scale data while maintaining privacy. Bowie State University Department of Computer Science
  4. 4. Signal Processing and Data Privacy Introduction: Privacy Definition • Privacy definition is problematic. • There are different meaning for these words across different communities: • Privacy • Confidentiality • Security • “There is no real separation between individuals’ identity and their data—the pattern of data associated with an individual is itself uniquely identifying.” Sarwate and Chaudhuri (2013) Bowie State University Department of Computer Science
  5. 5. Signal Processing and Data Privacy Differential Privacy Definition • Cryptographically Motivated. • Proposed by Cynthia Dwork (2006). • Imposes confidentiality by returning perturbed query responses from databases: • 𝒇 𝒙 + 𝑳𝒂𝒑𝒍𝒂𝒄𝒆(𝟎, 𝒃) • 𝒃= • ∆𝒇 𝜺 ∆𝒇 = 𝑴𝒂𝒙 𝒇 𝑫 𝟏 − 𝒇 𝑫 𝟐 • The end user of the database cannot know if a data item has been altered. • An attacker cannot gain information about any data item in the database. Bowie State University Department of Computer Science
  6. 6. Signal Processing and Data Privacy Differential Privacy Definition: ε-differential privacy is satisfied if the results to a query run on database D1 and D2 should probabilistically be similar, and meet the following condition:  𝑷[𝒒 𝒏 (𝑫 𝟏 )∈𝑹] 𝑷[𝒒 𝒏 𝑫 𝟐 ∈𝑹] ≤ 𝒆𝜺  Where D1 and D2 are the two databases   P is the probability of the perturbed query results D1 and D2. qn() is the privacy granting procedure (perturbation).   qn(D1) is the privacy granting procedure on query results from database D1. qn(D2) is the privacy granting procedure on query results from database D2.   R is the perturbed query results from the databases D1 and D2 respectively. 𝒆 𝜺 is the exponential e epsilon value. Bowie State University Department of Computer Science
  7. 7. Signal Processing and Data Privacy Differential Privacy Definition – Types • Query-based Differential Privacy. • Input perturbation based differential privacy – add Laplace noise to the data. • Output perturbation – add Laplace noise to the query results. Bowie State University Department of Computer Science
  8. 8. Signal Processing and Data Privacy Differential Privacy – Input and Output Perturbation Image Source: Sarwate, A.D.; Chaudhuri, K., "Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data," Signal Processing Magazine, IEEE , vol.30, no.5, pp.86,94, Sept. 2013 doi: 10.1109/MSP.2013.2259911 Bowie State University Department of Computer Science
  9. 9. Signal Processing and Data Privacy Differential Privacy Challenges • There is a tension between data privacy and data utility. • Evaluating the impact of privacy restrictions on utility. • Data dimensionality. • Trade-off points between data privacy and utility. Bowie State University Department of Computer Science
  10. 10. Signal Processing and Data Privacy Differential Privacy Challenges – Utility Quantification • Mean Squared Error – for statistical estimation. • • Computed as follows: Original and privatized datasets 𝑋 − 𝑋 ′ 𝑑 𝑗=1 𝑛 ′ 2 𝑖=1(𝑥 𝑖𝑗 −𝑥 𝑖𝑗 ) 𝑛𝑑 • Expected loss – for classification. • Comparative analysis – quantify various differential privacy algorithms. • Where n is the number of records and d the number of variables in the table. Bowie State University Department of Computer Science
  11. 11. Signal Processing and Data Privacy Differential Privacy Challenges – Limitations • Time Series and Filtering problems. • Understanding the fundamental limits for continuous data may shed some light on which signal processing tasks are possible under differential privacy. • The optimal differential privacy parameter adjustment for acceptable levels of data utility – adjustment of the 𝜺 value. • A single data set could be used in multiple computations – challenge is how to keep the same differential privacy across the board. Bowie State University Department of Computer Science
  12. 12. Signal Processing and Data Privacy Differential Privacy – Signal Processing Applications • Apply differential piracy in signal processing problems. • Apply signal processing to the differential privacy problem of data utility. • Time Series and Filtering problems. • Differential privacy of a query sequence in Fourier domain and use homomorphic encryption for distributed noise addition. • Kalman filtering applied to a differentially private time series. • Kalman filtering used on aggregated signals after input and output perturbation. Bowie State University Department of Computer Science
  13. 13. Signal Processing and Data Privacy Differential Privacy – Applications • Integrate differential privacy in signal processing. • Use signal processing to enhance data utility and privacy. • Use signal processing to filter out unneeded noise. • Research areas – applying differential privacy in signal processing for: • • • • Image processing Network information systems Cryptographic approaches Social networks Bowie State University Department of Computer Science
  14. 14. Signal Processing and Data Privacy Conclusion • A general overview of the differential privacy is given. • The paper focused on differential privacy and the applications in signal processing. • The paper suggests research areas for applying differential privacy in signal processing. • Implementation is left to the readers. Bowie State University Department of Computer Science
  15. 15. Signal Processing and Data Privacy References • Sarwate, A.D.; Chaudhuri, K., "Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data," Signal Processing Magazine, IEEE , vol.30, no.5, pp.86,94, Sept. 2013 doi: 10.1109/MSP.2013.2259911 • Mivule, K; Turner, C; and Y. Ji, S., “Towards A Differential Privacy and Utility Preserving Machine Learning Classifier,” in Procedia Computer Science, 2012, vol. 12, pp. 176–181. • Mivule, Kato, "Utilizing Noise Addition for Data Privacy, an Overview", Proceedings of the International Conference on Information and Knowledge Engineering (IKE 2012), Pages 65-71, Las Vegas, NV, USA. Bowie State University Department of Computer Science

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