2026-01-21 19:00:02
4 Identifying API Privacy-relevant Methods
5 Labels for Personal Data Processing
6 Process of Identifying Personal Data
7 Data-based Ranking of Privacy-relevant Methods
8 Application to Privacy Code Review
Conclusion, Future Work, Acknowledgement And References
\
Native privacy-relevant methods form the basis for identifying what we refer to as API privacyrelevant methods. These are methods found in third-party libraries and frameworks that are likely to process personal data by calling upon native privacy-relevant methods. Understanding the relationship between API and native methods is crucial for a complete review of how personal data is processed in a codebase.
\ The identification process is iterative and takes into account the dependencies between libraries and codebases, as depicted in Fig. 2. The goal is to assemble a list of API privacy-relevant methods that have the potential to handle personal data. Understanding the relationship and dependency hierarchy among these libraries is essential for accomplishing this task.
To manage library dependencies, we focus on import statements within each library’s source code. We organize the libraries in a sequence such that each library is evaluated only after all its dependencies have been assessed. This ensures a logical and efficient evaluation process. For the identification of API privacy-relevant methods, we define a set denoted as API.
\ This set includes methods from our organized list of libraries that invoke native privacy-relevant methods at some point during their execution. These methods are significant as they interact with native methods, either directly or through a chain of calls, making them critical for privacy code review.
Compliance with data protection regulations like GDPR necessitates a nuanced understanding of how personal data is processed within code. While GDPR outlines various processing activities such as collection, recording, and organization, the four native privacy-relevant method categories [8] we previously discussed (I/O, security, database, and network) lack the granularity needed for comprehensive understanding.
\ For instance, the security category encompasses both authentication and encryption, warranting a more detailed labeling system. After analyzing top labels from Maven and NPM that pertain to personal data processing, we identified 20 labels that closely align with both GDPR’s definitions and our native privacyrelevant method categories. This shows how libraries handle data processing in different ways. For example, OAuth combines network and security functionalities, while Object-Relational Mapping (ORM) bridges database and I/O operations.
\ These overlaps underscore the necessity for a detailed set of labels tailored for privacy reviews. We present these labels and their alignment with GDPR requirements in Table 1. These labels serve a dual purpose: they categorize methods involved in data processing activities like collection, storage, and encryption, and they map these activities to GDPR compliance requirements. This streamlined mapping simplifies the task of identifying code sections that need to comply with legal standards. In our later approach, we use these labels to prioritize privacy-relevant methods, enabling a focused review on areas critical for data protection.
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:::info Authors:
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:::info This paper is available on arxiv under CC BY-NC-SA 4.0 license.
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2026-01-21 18:09:19
\
What separates a cryptocurrency that endures for over a decade from the thousands that vanish within their first year?
\ January 18, 2025 marks twelve years since Dash launched as a fork of Bitcoin, making it one of the oldest active blockchain networks still operating with its original vision intact. While data from CoinGecko shows that over 90% of cryptocurrencies launched since 2017 no longer maintain active development or trading volume, Dash continues processing transactions daily across 158 countries. The network has maintained continuous operation since 2013, outlasting projects that once held higher market capitalizations and generated more media attention.
\
The cryptocurrency industry operates with a failure rate that exceeds traditional startups. Research from Boston College found that 80% of initial coin offerings between 2017 and 2018 failed to maintain any value or development activity beyond 18 months. These failures stem from three primary causes: teams that abandon development after raising funds, technology that fails to deliver promised features, and networks that cannot sustain enough user activity to justify continued operation.
\ Dash entered a market where Bitcoin already dominated the payment use case and Litecoin had established itself as the faster alternative. The project differentiated itself through a two-tier network structure that split functions between miners who secure the blockchain and masternodes that enable additional features. This architecture allowed Dash to implement InstantSend for near-instant transaction confirmation and PrivateSend for optional transaction privacy, both features that required more than simple code changes to Bitcoin.
\ The network's funding mechanism allocates 10% of each block reward to a treasury that masternode operators vote on for development proposals. Since implementation, this system has distributed over $200 million to development teams, marketing initiatives, and integration partners according to blockchain records. Unlike projects dependent on venture capital or foundation reserves that eventually deplete, Dash generates ongoing revenue from its block rewards, creating a sustainable funding model that adapts to network value.
\
Dash initially positioned itself as digital cash for everyday transactions, competing directly with Bitcoin's payment narrative. The project gained merchant adoption in Venezuela during the country's hyperinflation period, where local transaction volume peaked at over 55,000 monthly transactions in 2019. However, as Bitcoin's narrative shifted toward store of value and Ethereum demonstrated the potential for programmable money, Dash faced an identity challenge.
\ The network's response involved expanding beyond simple payments while maintaining its core functionality. Dash Platform, currently in testing on mainnet, introduces decentralized identity and data storage capabilities that allow developers to build applications directly on Dash infrastructure. This evolution mirrors Ethereum's transition from a payment system to a development platform, though Dash maintains its focus on user experience and transaction speed rather than complex smart contract functionality.
\ The platform introduces usernames that replace complex wallet addresses, state transitions that enable data updates without storing everything on the blockchain, and a decentralized API that applications can query without running full nodes. These features address usability barriers that have prevented mainstream blockchain adoption, targeting use cases from social media to business process management. \n
Dash has survived four distinct cryptocurrency market cycles, each bringing different challenges and competitive threats. The 2017 ICO boom saw hundreds of projects raise more funding than Dash's entire market capitalization, yet most failed to deliver working products. The 2020-2021 DeFi summer shifted attention to yield farming and decentralized exchanges, temporarily reducing interest in payment-focused cryptocurrencies. The 2022 collapse of Terra, Celsius, and FTX demonstrated the risks of unsustainable tokenomics and centralized custody.
\n
Throughout these cycles, Dash maintained its network operation, continued development, and preserved its decentralized governance structure. The network currently operates with 3,850 active masternodes globally, each requiring 1,000 DASH as collateral. This distribution prevents single entities from controlling the network's direction or treasury allocation, though it also slows decision-making compared to centralized development teams. \n
The project's longevity offers data on what sustains blockchain networks beyond initial hype. Consistent development funding, alignment between stakeholders through governance participation, and focus on specific use cases rather than attempting to solve every problem appear as common factors. Dash's masternode operators have financial incentives to support proposals that increase network value, creating a feedback loop between governance decisions and token price that doesn't exist in pure proof-of-work systems. \n
Dash's twelve-year operation demonstrates that cryptocurrency projects can survive beyond their initial vision when they maintain development momentum and adapt to market changes without abandoning core principles. The network has processed millions of transactions, funded hundreds of development proposals, and maintained decentralized governance through multiple market cycles that eliminated most competitors from its era.
\n
The cryptocurrency industry's high failure rate makes any project's twelfth anniversary noteworthy. Whether Dash achieves mainstream adoption or remains a specialized payment network depends on execution of its platform features and competition from newer projects with better funding or technology. But the project has already answered the question that most cryptocurrencies never reach: how to build something that lasts beyond the initial speculation. \n
Don't forget to like and share the story!
:::tip This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO
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2026-01-21 17:00:02
4 Identifying API Privacy-relevant Methods
5 Labels for Personal Data Processing
6 Process of Identifying Personal Data
7 Data-based Ranking of Privacy-relevant Methods
8 Application to Privacy Code Review
Conclusion, Future Work, Acknowledgement And References
\
Code review, originally aimed at ensuring software quality by identifying bugs and performance issues [11], has expanded to address security vulnerabilities and, more recently, privacy concerns under data protection laws like the GDPR. Privacy-focused reviews add the complexity of ensuring personal data is handled lawfully and ethically, a challenging task due to the often ambiguous nature of data protection guidelines [10].
\ Static analysis tools are pivotal in code reviews, aiding in the identification of data flows, security risks, and compliance issues. The effectiveness of a review is measured by its ability to pinpoint critical problems and offer actionable solutions. Privacy code reviews, however, struggle with identifying personal data due to unclear definitions and varied contexts, increasing reliance on these tools despite their limitations in recognizing diverse personal data types [9].
\ These reviews also play a key role in creating essential compliance documents like Records of Processing Activities (ROPA) and Data Protection Impact Assessments (DPIA). The proposed automated approach in this paper focuses on improving the efficiency and accuracy of privacy code reviews, specifically in categorizing personal data processing in large-scale code projects.
To streamline the process of privacy code review, we introduce the concept of privacy-relevant methods. These are specific methods that play a direct role in the processing of personal data. Such methods can be part of standard libraries or third-party libraries, making them critical focal points for personal data processing in software applications. Native libraries are foundational because they offer the only pathways to device resources like files and networks.
\ Consequently, any operation involving data storage or transfer must go through these native methods. Native privacy-relevant methods are those found in standard libraries of programming languages like JavaScript and Java. These methods act as the origins (sources) for all personal data entered by users via devices. They are also the exclusive methods that directly transmit this data to other devices or services. We categorize these native methods into domains such as I/O, Database, Network, Security, following the guidelines of existing research [8].
\ We identify these methods through a systematic manual review that includes an examination of documentation, source code, and actual usage patterns. To facilitate the identification and categorization of native privacy-relevant methods, we conducted an in-depth analysis of key modules like java.io, java.security, and java.util for Java, and their equivalents in JavaScript. This analysis helps us compile a complete set of native privacy-relevant methods, denoted as Native, that are involved in personal data processing.
\
:::info Authors:
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:::info This paper is available on arxiv under CC BY-NC-SA 4.0 license.
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2026-01-21 16:00:07
:::info Authors:
:::
4 Identifying API Privacy-relevant Methods
5 Labels for Personal Data Processing
6 Process of Identifying Personal Data
7 Data-based Ranking of Privacy-relevant Methods
8 Application to Privacy Code Review
Conclusion, Future Work, Acknowledgement And References
Privacy code review is a critical process that enables developers and legal experts to ensure compliance with data protection regulations. However, the task is challenging due to resource constraints. To address this, we introduce the concept of privacy-relevant methods — specific methods in code that are directly involved in the processing of personal data. We then present an automated approach to assist in code review by identifying and categorizing these privacy-relevant methods in source code. Using static analysis, we identify a set of methods based on their occurrences in 50 commonly used libraries.
\ We then rank these methods according to their frequency of invocation with actual personal data in the top 30 GitHub applications. The highest-ranked methods are the ones we designate as privacy-relevant in practice. For our evaluation, we examined 100 opensource applications and found that our approach identifies fewer than 5% of the methods as privacy-relevant for personal data processing. This reduces the time required for code reviews. Case studies on Signal Desktop and Cal.com further validate the effectiveness of our approach in aiding code reviewers to produce enhanced reports that facilitate compliance with privacy regulations.
In the realm of software development, privacy code reviews have become indispensable, especially with the advent of stringent data protection regulations like the General Data Protection Regulation (GDPR). Unlike security code reviews, which focus on existing security flaws or vulnerabilities, privacy code reviews are concerned with the ethical and lawful handling of personal data. Although there may be overlaps, such as in access control, the primary objectives of these two types of reviews are distinct: security reviews aim to prevent unauthorized access, while privacy reviews aim for compliance with data protection principles. Privacy code reviews involve a systematic process where source code is inspected to trace the flow of personal data.
\ Equipped with program analysis tools, reviewers categorize these flows and detail how personal data is processed. This analysis serves as a comprehensive guide for compliance checks and aids Data Protection Officers (DPOs) in fulfilling their responsibilities. The process is illustrated in Figure 1. However, the challenge arises from the complexity and sheer volume of modern codebases, making it difficult to identify instances where personal data is processed.
\ Recent studies [6, 7] have examined tools for identifying personal data, but less focus has been placed on data that is dynamically changing or in active use. While categorizations exist for personal data itself, taxonomies of the processing code are lacking. Developing a understanding of the diverse ways data can be handled would illuminate processing activities and facilitate compliance reporting like records of processing activities (ROPA) and data protection impact assessments (DPIA). Since reviewing entire codebases is time-consuming, targeting reports to highlight the most relevant aspects could better serve reviewers and streamline the compliance process. The goal should be
\ providing clarity on key data handling activities without getting lost in an elaborate labeling framework. In light of these challenges, we propose an automated approach to enhance the efficiency and effectiveness of privacy code reviews. Our approach focuses on identifying privacy-relevant methods — specifically, Java methods or JavaScript functions commonly found in popular libraries — that are involved in the processing of personal data. By doing so, we can pinpoint instances in real-world applications where these privacy-relevant methods are invoked to handle personal data.
\ This paper addresses the following research questions:
How to identify privacy-relevant methods in commonly used libraries that potentially process personal data?
How to categorize such privacy-relevant methods based on their actual usage in real-world applications? To answer these questions, we make the following contributions:
\
We present a novel static analysis technique specifically designed to identify methods in source code that are involved in the processing of personal data. (Section 4)
We develop a set of labels for categorizing personal data and the methods that process them, thereby providing a structured approach to understanding how personal data is processed in code. (Sections 5 and 6)
\
We apply our approach to a set of popular open-source applications. Through this, we rank privacy-relevant methods based on their frequency of occurrence, thereby identifying those that are most critical for privacy considerations. (Section 7)
\
We provide insights to code reviewers by highlighting frequently used methods relevant to privacy, based on our large-scale study and specific case studies. This approach streamlines the review process, enabling a more focused and efficient identification of potential privacy risks. (Section 8) Our evaluation of 100 open-source applications indicates that our approach identifies fewer than 5% of methods involved in personal data processing as privacy-relevant methods. This enables reviewers to focus only on the identified relevant code, thereby expediting privacy code reviews.
\
:::info This paper is available on arxiv under CC BY-NC-SA 4.0 license.
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2026-01-21 15:10:57
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2026-01-21 14:43:11
Atlanta, GA, United States, January 20th, 2026/CyberNewsWire/--Airlock Digital, a leader in proactive application control and endpoint security, announced the release of The Total Economic Impact (TEI) of Airlock Digital, an independent study commissioned by Airlock Digital and conducted by Forrester Consulting.
The study demonstrates a significant 224% return on investment (ROI) and a $3.8 million net present value (NPV) over three years for organizations adopting Airlock Digital’s allowlisting approach. These findings underline both the financial and security value of Airlock Digital’s solution.
Forrester’s TEI methodology evaluates the potential financial impact of technology investments by aggregating insights from customer interviews and modeling a composite organization representative of global organizations. According to the study, Airlock Digital enabled:
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David Cottingham, Co-founder and CEO at Airlock Digital, said: “For modern enterprises, trust cannot be assumed… it must be enforced. Allowlisting and application control give organizations the power to run only what they trust, blocking all malware and ransomware before they can execute.
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For us, the Forrester Consulting TEI study reinforces the importance of our mission at Airlock Digital, which is to deliver proactive endpoint security that makes application control not just possible, but effortless. It’s why we have become synonymous with this critical layer of cyber defense—and why every organization needs it at the core of their security strategy.”
As cyberattacks continue to grow in scale and sophistication, more organizations are turning to application control and allowlisting as foundational components of a proactive security strategy. Traditional reactive security tools attempt to detect and block threats after execution attempts are made—often too late to prevent compromise.
Allowlisting reverses this paradigm, enforcing a Deny by Default posture that ensures only trusted and approved software is permitted to run. This approach dramatically reduces the attack surface, curbs the spread of malware and ransomware, and helps organizations meet increasingly stringent regulatory and compliance requirements.
Airlock Digital’s modern, operationally friendly implementation of allowlisting enables security teams to adopt this strategy without the administrative complexity historically associated with legacy tools.
The study highlights that Airlock Digital helps organizations strengthen their security posture, lower ongoing maintenance costs, and improve software inventory management while keeping operational and administrative burden low.
The study noted that a single security analyst can effectively manage Airlock Digital policies in much less time than traditional solutions require, contributing to cost savings and improved productivity.
Patrick Dillon, CRO at Airlock Digital said: “The Forrester Consulting TEI study gives security leaders, in our opinion, clear, independent validation of the impact delivered by Airlock Digital. Forrester Consulting calculated the benefits to include a 224% ROI and fast payback — and most importantly — participating organizations reported zero breaches after implementation. Airlock Digital combines simplicity with enterprise-grade scale, enforcing a Deny by Default posture that blocks untrusted code, including malware and ransomware. For organizations ready to move from reactive defenses to proactive prevention, Airlock Digital provides a quantified and operationally efficient path forward — requiring, according to the Forrester Consulting study, only 2.5 hours per week to manage. We’d be glad to walk you through the findings."
Airlock Digital delivers market-leading allowlisting and application control solutions that empower enterprises to enforce a Deny by Default security posture. Trusted globally across industries, Airlock Digital enables organizations to prevent unauthorized code execution, simplify compliance, and strengthen cyber-resilience without sacrificing performance or user productivity.
This approach minimizes attack surfaces and helps organizations align their cybersecurity strategies with government frameworks and standards.
Users can download the full Forrester TEI study: https://www.airlockdigital.com/forrester-tei-report
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:::tip This story was published as a press release by Cybernewswire under HackerNoon’s Business Blogging Program. Do Your Own Research before making any financial decision.
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