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The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling.
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L · 1965
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Sorting Networks and their Applications
Batcher, K. E · 1968
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The Protection of Information in Computer Systems
Saltzer, J. H., and Schroeder, M. D · 1975
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How to Share a Secret
Shamir, A · 1979
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Cryptography and Data Security
Denning, D. E. R · 1982
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Tight Bounds on the Complexity of Parallel Sorting
Leighton, T · 1985
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Generalizing Data to Provide Anonymity when Disclosing Information (Abstract)
Samarati, P., and Sweeney, L · 1998
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Digital Signature Standard (DSS)
National Institute of Standards and Technology · 2000
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Protecting Respondents’ Identities in Microdata Release
Samarati, P · 2001
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The Sybil attack
Douceur, J. R · 2002
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Secure History Preservation Through Timeline Entanglement
Maniatis, P., and Baker, M · 2002
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Network Security with OpenSSL
Viega, J., Chandra, P., and Messier, M · 2002
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Relaxing the Problem-Size Bound for Out-Of-Core ColumnSort
Chaudhry, G., Hamon, E. A., and Cormen, T. H · 2003
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Limiting Privacy Breaches in Privacy Preserving Data Mining
Evfimievski, A., Gehrke, J., and Srikant, R · 2003
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Trusted Computing, Trusted Third Parties, and Verified Communications
Abadi, M · 2004
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Provable Anonymity for Networks of Mixes
Klonowski, M., and Kutyłowski, M · 2005
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Scalable Statistical Bug Isolation
Liblit, B., Naik, M., Zheng, A. X., Aiken, A., and Jordan, M. I · 2005
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Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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l l -diversity: Privacy Beyond k k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., and Venkitasubramaniam, M · 2007
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Differential Privacy: A Survey of Results
Dwork, C · 2008
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Composition Attacks and Auxiliary Information in Data Privacy
Ganta, S. R., Kasiviswanathan, S. P., and Smith, A · 2008
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Robust De-anonymization of Large Sparse Datasets
Narayanan, A., and Shmatikov, V · 2008
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Debugging in the (Very) Large: Ten Years of Implementation and Experience
Glerum, K., Kinshumann, K., Greenberg, S., Aul, G., Orgovan, V., Nichols, G., Grant, D., Loihle, G., and Hunt, G · 2009
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Privacy Integrated Queries: An Extensible Platform for Privacy-Preserving Data Analysis
McSherry, F. D · 2009
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Browser Security: Lessons from Google Chrome
Reis, C., Barth, A., and Pizano, C · 2009
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Differentially-Private Network Trace Analysis
McSherry, F., and Mahajan, R · 2010
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Airavat: Security and Privacy for MapReduce
Roy, I., Setty, S. T. V., Kilzer, A., Shmatikov, V., and Witchel, E · 2010
Cited alongside, same era.
Catch Me if You Can: Performance Bug Detection in the Wild
Jovic, M., Adamoli, A., and Hauswirth, M · 2011
Cited alongside, same era.
Information Needs for Software Development Analytics
Buse, R. P. L., and Zimmermann, T · 2012
Cited alongside, same era.
Towards Statistical Queries over Distributed Private User Data
Chen, R., Reznichenko, A., Francis, P., and Gehrke, J · 2012
Cited alongside, same era.
Crowd-Blending Privacy
Gehrke, J., Hay, M., Lui, E., and Pass, R · 2012
Cited alongside, same era.
On Sampling, Anonymization, and Differential Privacy or, k k -anonymization Meets Differential Privacy
Li, N., Qardaji, W., and Su, D · 2012
Cited alongside, same era.
Free for All! Assessing User Data Exposure to Advertising Libraries on Android
Demetriou, S., Merrill, W., Yang, W., Zhang, A., and Gunter, C. A · 2016
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Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries
Fanti, G., Pihur, V., and Erlingsson, Ú · 2016
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PSI ( Ψ \Psi ): A Private data Sharing Interface
Gaboardi, M., Honaker, J., King, G., Nissim, K., Ullman, J., and Vadhan, S. P · 2016
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Inside the Cyberattack That Shocked the US Government
Koerner, B. I · 2016
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SoK: Privacy on Mobile Devices—It’s Complicated
Spensky, C., Stewart, J., Yerukhimovich, A., Shay, R., Trachtenberg, A., Housley, R., and Cunningham, R. K · 2016
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GUPT: Privacy Preserving Data Analysis Made Easy
Mohan, P., Thakurta, A., Shi, E., Song, D., and Culler, D · 2012
Cited alongside, same era.
AppInsight: Mobile App Performance Monitoring in the Wild
Ravindranath, L., Padhye, J., Agarwal, S., Mahajan, R., Obermiller, I., and Shayandeh, S · 2012
Cited alongside, same era.
Message-Locked Encryption for Lock-Dependent Messages
Abadi, M., Boneh, D., Mironov, I., Raghunathan, A., and Segev, G · 2013
Cited alongside, same era.
Message-Locked Encryption and Secure Deduplication
Bellare, M., Keelveedhi, S., and Ristenpart, T · 2013
Cited alongside, same era.
Carat: Collaborative Energy Diagnosis for Mobile Devices
Oliner, A. J., Iyer, A. P., Stoica, I., Lagerspetz, E., and Tarkoma, S · 2013
Cited alongside, same era.
Panappticon: Event-Based Tracing to Measure Mobile Application and Platform Performance
Zhang, L., Bild, D. R., Dick, R. P., Mao, Z. M., and Dinda, P · 2013
Cited alongside, same era.
Vallina-Rodriguez, N., Sundaresan, S., Razaghpanah, A., Nithyanand, R., Allman, M., Kreibich, C., and Gill, P · 2016
Later among the works it cites.
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
Abadi, M., Erlingsson, Ú., Goodfellow, I., McMahan, H. B., Papernot, N., Mironov, I., Talwar, K., and Zhang, L · 2017
Closest in time.
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
Avent, B., Korolova, A., Zeber, D., Hovden, T., and Livshits, B · 2017
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Practical Locally Private Heavy Hitters
Bassily, R., Nissim, K., Stemmer, U., and Thakurta, A · 2017
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Plausible Deniability for Privacy-Preserving Data Synthesis
Bindschaedler, V., Shokri, R., and Gunter, C. A · 2017
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Practical Secure Aggregation for Privacy Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
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Telling Your Secrets without Page Faults: Stealthy Page Table-Based Attacks on Enclaved Execution
Bulck, J. V., Weichbrodt, N., Kapitza, R., Piessens, F., and Strackx, R · 2017
Closest in time.
RAPPOR (Randomized Aggregatable Privacy Preserving Ordinal Responses)
Chromium Projects · 2017
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Prio: Private, Robust, and Scalable Computation of Aggregate Statistics
Corrigan-Gibbs, H., and Boneh, D · 2017
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Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute
Dang, H., Dinh, T. T. A., Chang, E.-C., and Ooi, B. C · 2017
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gRPC: A High Performance, Open-Source Universal RPC Framework
Google · 2017
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https://software.intel.com/en-us/sgx-sdk , 2017
Intel® Software Guard Extensions (Intel® SGX) SDK · 2017
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Towards Practical Differential Privacy for SQL Queries
Johnson, N., Near, J. P., and Song, D · 2017
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Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch Shadowing
Lee, S., Shih, M.-W., Gera, P., Kim, T., Kim, H., and Peinado, M · 2017
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Glimmers: Resolving the Privacy/Trust Quagmire
Lie, D., and Maniatis, P · 2017
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Maniatis, P., Mironov, I., and Talwar, K · 2017
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How to Protect Yourself from that Massive Equifax Breach
Newman, L. H · 2017
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The Long-Standing Privacy Debate: Mobile Websites vs Mobile Apps
Papadopoulos, E. P., Diamantaris, M., Papadopoulos, P., Petsas, T., Ioannidis, S., and Markatos, E. P · 2017
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Privacy Loss in Apple’s Implementation of Differential Privacy on macOS 10.12
Tang, J., Korolova, A., Bai, X., Wang, X., and Wang, X · 2017
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Locally Differentially Private Protocols for Frequency Estimation
Wang, T., Blocki, J., Li, N., and Jha, S · 2017
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Opaque: An Oblivious and Encrypted Distributed Analytics Platform
Zheng, W., Dave, A., Beekman, J. G., Popa, R. A., Gonzalez, J. E., and Stoica, I · 2017
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