Timestamp Modification for Inference Attack Prevention

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Solution Overview

Problem

Computer systems face challenges in ensuring the confidentiality and accuracy of sensitive data, such as salary information, due to concerns about data misuse and privacy, leading to reluctance from users to share such information, and difficulties in preventing timestamp-based inference attacks.

Innovation Solution

A system is implemented that uses a confidential data frontend to collect and transmit encrypted data, with separate encryption keys for user identification and data, and employs methods like k-anonymity and random timestamp modification to prevent inference attacks, ensuring secure storage and anonymization of data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If timestamp information is recorded for data submissions, then data accuracy and reliability are improved, but users become vulnerable to timestamp-based inference attacks that compromise privacy

Engineering Contradiction:

Improvedata accuracyVSAvoidinference attacks

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system applies preliminary anti-action by proactively adding random noise to timestamps before they are stored or processed. This pre-emptive modification prevents the timestamps from being usable in inference attacks, while still preserving their utility for basic temporal analysis and data quality assurance.

Inventive Principle:

Principle #9Preliminary anti-action

Solution Approach 2:

Random noise acts as an intermediary between the original timestamp and the stored timestamp value. This intermediary layer obscures the precise timing information that could be used for inference attacks, while still maintaining sufficient temporal information for data accuracy and reliability checks.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Quantity of substance

If users are encouraged to share confidential data, then data quantity and statistical analysis quality are improved, but user privacy and security concerns increase

Engineering Contradiction:

Improvedata quantityVSAvoidprivacy concerns

Core Design Contradiction:

Quantity of substanceVSObject-affected harmful factors

Solution Approach 1:

The system converts the potentially harmful precise timestamp information into beneficial obscured timestamps by adding random noise. This transformation maintains the utility of temporal data for analysis while eliminating the privacy risk, effectively turning a harmful feature into a beneficial one.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Solution Approach 2:

The system changes the parameter of timestamp precision by adding random noise, transforming exact timestamps into obscured timestamps. This parameter change reduces the information available for inference attacks while preserving sufficient temporal information for data quality assurance and statistical analysis.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10360372B1Preventing timestamp-based inference attacks

Publication Date: 2019.07.23 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

In an example embodiment, a method for protecting against timestamp-based inference attacks in a computer system is provided. A timestamp corresponding to a time when confidential data is submitted to the computer system by a user is recorded. A modification value based on a frequency of submissions of confidential data to the computer system is selected. The timestamp is altered by adding the modification value to the timestamp.