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Zero‑knowledge proofs ineducation:
apathway todisability inclusion andequitable
learning opportunities
Xiao Xu1*
Introduction
e concept of Zero-Knowledge Proofs (ZKPs) originated in cryptography as a compo-
nent of interactive proof systems. A ZKP allows one party, the prover, to demonstrate
to another party, the verifier, that they possess certain knowledge without revealing
the information itself (Goldwasser etal., 1985). In identification schemes, the proof of
knowledge can be established using computational identification protocols, moving
beyond certifying mere assertions. Recently, this concept has been adopted by block-
chain platforms as a means to prove possession of sensitive data without revealing it,
addressing growing privacy concerns. Some applications include data-minimized anon-
ymous credentials in digital wallets (Babel & Sedlmeir, 2023) and the use of blockchain-
based ZKP in city traffic management systems (Li etal., 2020). While the educational
Abstract
In the evolving landscape of global education, the significance of inclusivity and equity
has never been more important. Emphasizing the United Nation Sustainable Develop-
ment Goal 4, this paper explores the innovative application of blockchain-powered
Zero-Knowledge Proofs (ZKPs) technology in education, with a particular focus on dis-
ability inclusion. This study introduces a novel disability management system powered
by Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK).
This advanced system enables educational institutions to verify the status of students
with disabilities without compromising their personal information, thereby preserving
their privacy and reinforcing their identity. This paper evaluates the potential opera-
tional efficiency of this prototype system against the existing costs incurred by higher
education institutions in disability schemes. It also examines the system’s potential
to enhance self-disclosure among students with disability, which is pivotal for their aca-
demic success. By advocating for privacy and inclusivity, this study highlights the trans-
formative potential of ZKP in creating an educational environment where students
with disabilities can comfortably disclose their needs. This approach not only protects
their confidentiality but also empowers them academically, aligning with the global
commitment to accessible and inclusive education.
Keywords: Blockchain, Zero-knowledge proof, Disability inclusion
Open Access
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RESEARCH
Xu Smart Learning Environments (2024) 11:7
https://doi.org/10.1186/s40561‑024‑00294‑w
Smart Learning Environments
*Correspondence:
x.xu@unsw.edu.au
1 UNSW Sydney Business School,
University of New South Wales,
Kensington, NSW 2052, Australia
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Xu Smart Learning Environments (2024) 11:7
sector has seen numerous applications of blockchain, the ZKP technology has yet to
be fully studied. is paper explores the potential of ZKPs to support inclusive edu-
cation, in line with the United Nations Sustainable Development Goal 4 (UN SDG 4),
which aims to ensure inclusive and equitable quality education for all. is leads us to
the central research question: ‘How can the application of ZKP technology improve the
confidentiality and protection of sensitive data for students with disabilities?’ We will
specifically examine how implementing ZKP systems can enhance self-disclosure among
these students and contribute to their academic success, thereby aligning with UN SDG
4 objectives. Additionally, we will explore how ZKPs can improve efficiency in university
administrative operations.
Literature review
Blockchain technology, through its distributed ledger system, offers a method for stor-
ing and sharing information among participants. Transaction records on the blockchain
are permanent, transparent, and immutable. Once a block of transactions is verified and
added to the blockchain, it cannot be altered. Over the past decade, blockchain tech-
nology has seen significant growth with applications such as InterPlanetary File System
(IPFS), smart contracts, and decentralized finance (DeFi) among others.
In the realm of education, blockchain technology offers numerous potential appli-
cations due to its inherent trustworthiness, immutability, transparency, and self-sov-
ereignty. One of its most widely recognized applications in recent years pertains to
certificate management, the tracking of competencies, and the definition of learning
objectives, all of which bolster the concept of lifelong learning. e capability of block-
chain to maintain comprehensive lifelong learning logs allows learners to prioritize the
storage of detailed data over merely possessing a diploma (Ocheja et al., 2019). Uni-
versity credits, which resemble some features of tokens in the blockchain world, can
be authenticated by blockchain-powered higher education institutions. is capability
enables universities to seamlessly issue degrees via the blockchain (Grech & Camilleri,
2017). Additionally, academic records can be securely shared and verified (Arenas & Fer-
nandez, 2018), making credit transfers for students more efficient due to the immutable
nature of records on a public ledger (Turkanović etal., 2018).
Raimundo and Rosário (2021) noted that in addition to supporting the accreditation of
authentic certificates or academic data, blockchain technology promotes a decentralized
learning infrastructure for all stakeholders on the network. is makes it a valuable tool
for various educational processes. Furthermore, it can also provide feedback for teacher
evaluations through carefully designed smart contracts (Chen etal., 2018). e details of
smart contract-based learning system can be developed to fit different online learning
purposes (H. Sun etal., 2018; X. Sun etal., 2021b). A peer-to-peer (P2P) network can
be developed to bridge the gap between the academic theory and real-world practices
(Lizcano etal., 2020). e prospect of low-cost blockchain accreditation for work-based
learning achievements is particularly appealing (Williams, 2019).
Despite the advantages, Alammary etal. (2019) highlighted nine different challenges
that blockchain may face, including scalability. As the number of blocks increases, trans-
action latency will also increase. Another significant challenge is privacy, which is fur-
ther complicated by security concerns such as cyber risks and data leaks. Li etal. (2022)
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Xu Smart Learning Environments (2024) 11:7
emphasized that all transaction records in the blockchain must be disclosed to all nodes,
which significantly increases the risk of privacy leakage. ey also highlighted the poten-
tial use of ZKP as a solution to protect privacy and ensure calculation verification. e
ZKP protocol enables the prover to confirm the accuracy of a statement to a verifier
without revealing the underlying information. is not only enhances data privacy and
confidentiality but also provides benefits such as reduced computations, since validators
only need to verify the proofs (Berentsen etal., 2023). ZKP has been increasingly inte-
grated into blockchain technology to improve scalability and address the issue of high
transaction gas fees. Methods such as ZKP are recommended to safeguard the privacy
of biometric data for identification, with an emphasis on intelligent proctoring systems
and identification methods (Portugal etal., 2023). e ZKP computational structure can
address issues related to personal data privacy when exchanging information between
the educational agencies (Yin, 2023). However, to the best of the authors’ knowledge, the
potential of ZKP to support inclusive education especially in relation to disability has yet
to be fully explored.
e United Nations has defined Sustainable Development Goal 4 (SDG 4) as ensur-
ing inclusive and equitable quality education for all (United Nations, 2015). Kwok and
Treiblmaier (2022) emphasize the potential of blockchain technology in fostering social
inclusion and enhancing access to education. Alongside blockchain, the advent of mobile
technologies, cloud computing, the Internet of ings (IoT), and artificial intelligence
has seen Information and Communication Technologies (ICTs) increasingly support
students with disabilities (Fichten etal., 2020). Successful implementation of these tech-
nologies can greatly benefit such students, fostering inclusion in the classroom (Fernán-
dez-Cerero etal., 2023; Perera-Rodríguez & Moriña Díez, 2019). However, the training
of teaching staff becomes crucial when introducing new technologies. Without proper
adaptation and training, there is a risk that these tools might inadvertently become
obstacles, potentially leading to the marginalization of students with disabilities.
Due to technological advancements, the construction of self and identity processes are
undergoing significant shifts. Maintaining anonymity and ensuring the privacy of certain
information have become key concerns in the digital age (Muñoz-Rodríguez etal., 2022).
L. Li and Ruppar (2021) highlight that an inclusive teacher identity is a foundational pil-
lar in the conceptual framework for inclusive education, promoting equal status in col-
laborative teaching partnerships. While most disabilities do not prevent students from
pursuing higher education, individual competencies can differ widely. Such differences
necessitate various accommodations and require a case-by-case analysis for special con-
siderations. is further elaborates on the challenges faced by the behavior intervention
teams when addressing individuals with psychiatric disabilities, particularly in relation
to potential overreporting and confidentiality issues.
In this paper, we explore the application of ZKPs in supporting students with disabili-
ties through a three-fold approach. First, we analyze the structure of the ZKP algorithm,
highlighting its inherent design for privacy protection. is analysis provides technical
insights, positioning ZKP as a promising tool for safeguarding sensitive student data,
especially for those with disabilities. Second, we evaluate the operational benefits of
integrating a prototype ZKP system into administrative processes within educational
institutions, focusing on efficiency gains and gas fees on the Ethereum network. ird,
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Xu Smart Learning Environments (2024) 11:7
we examine how ZKPs can facilitate self-disclosure for students with disabilities, con-
tributing to the establishment of their identities and enhancing academic outcomes. is
study underscores the role of ZKP in promoting inclusive education.
Methods
ZKPs are a cutting-edge technology designed to enhance privacy by minimizing the
amount of information shared between users in digital interactions. In addition, ZKPs
expedite the verification process in open systems by omitting unnecessary details. In this
section, we outline the algorithmic framework of ZKPs, presenting both the structural
architecture and the specific pseudo-code for disability management design within the
blockchain world. We also examine the implementation of a specific non-interactive
ZKP type, known as Zero-Knowledge Succinct Non-Interactive Argument of Knowl-
edge (zk-SNARKs), which greatly enhances scalability on the Ethereum network.
Algorithmic structure forprivacy
In the ZKP process, the primary parties involved are the prover, who aims to vali-
date their knowledge of a secret without revealing it, and the verifier, who assesses the
validity of the prover’s claim without accessing sensitive information. is mechanism
encompasses a four-stage procedure: commitment, challenge, proof, and validation, as
outlined in Table1 (Chi etal., 2023).
One of the commonly used illustrations to explain the ZKP protocol is the Ali Baba
Cave example (Quisquater etal., 1990). In the city of Baghdad, Ali Baba, discovers a
mysterious cave with two passages labeled A and B, each leading to what appears to be
a dead-end door, as shown in Fig.1. Each time he pursued a thief into the cave, the thief
would vanish, no matter which passage Ali Baba chose to search.
Table 1 ZKP Four-Step Algorithm
Step Purpose
Commit () → r The algorithm provides a commitment r, which can verify the correctness of the proof
about the secret s
Challenge () → e The algorithm creates a random challenge e, which the verifier sends to the prover
Prove (e, ω, k) → s The algorithm yields a proof s computed using the given e, the witness ω, and a random
string k
Verify (r, e, s) → {1, 0} The verification algorithm returns 1 if the verification result is accurate; if not, it returns 0
Fig. 1 Ali Baba Cave illustration
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Xu Smart Learning Environments (2024) 11:7
After many failed attempts, Ali Baba discovers a hidden mechanism: by whispering
a magic word "Open sesame," a hidden door opens as shown in Fig.2, connecting the
two passages. is secret allows the thieves to consistently evade Ali Baba by switching
between the passages. e enigma of this cave emphasizes the theme of proving knowl-
edge without revealing the secret.
In this example, the four-step algorithm can be analyzed as follows:
• Commit: After being robbed repeatedly, Ali Baba observes the thieves’ pattern of
escaping into the cave. Upon entering, each thief commits to a path, either A or B.
• Challenge: Seeking to catch the thief, Ali Baba enters the cave and must choose
which path to search: A or B. His choice represents the challenge.
• Prove: In response to Ali Baba’s challenge, the thief must prove their knowledge of
the cave’s secret. By using the magic words to open the secret door, the thief can
escape depending on the path Ali Baba selected.
• Verify: Observing the consistent success of the thieves in evading him, Ali Baba con-
cludes that they have knowledge of the cave’s secret. eir consistent ability to escape
through the opposite path serves as the confirmation of the knowledge.
rough probabilistic evaluations, Ali Baba’s confidence in the thief’s knowledge of the
cave’s secret grows. e more frequently the thief successfully disappears, the stronger
Ali Baba’s conviction becomes. However, this method does not provide definitive proof
in the same way that disclosing all the information would.
e aforementioned algorithm can be generalized to a situation where a student with
a disability needs to demonstrate to a university (symbolized by the cave) that their need
for specific accommodations due to their condition. ey aim to keep the exact nature
of their disability confidential. Every time the student engages with the university, they
commit to a particular path: either A (representing one type of proof) or B (indicating
another type of proof). e university, in its pursuit of ensuring equitable treatment,
challenges the student to prove their need for accommodations without revealing their
exact disability.
e university can opt to challenge the student via path A or B, each representing dif-
ferent methods of verification. In response to the university’s challenge, the student can
use the magic words (akin to a digital signature) that validate their need without disclos-
ing the specifics of their disability. is act of providing the correct proof in response
to the university’s challenge serves as evidence of their genuine need. By observing the
Fig. 2 Ali Baba Cave illustration with hidden door
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Xu Smart Learning Environments (2024) 11:7
consistent pattern of responses and the legitimacy of the proofs provided, the university
can confirm the student’s genuine need for accommodations. e student’s consistent
ability to provide the correct proof serves as confirmation of this knowledge.
It presents the blockchain-based ZKP network structure for students’ disability man-
agement in Fig.3. Each time the university sends a request to the prover (students with
a disability), the students create proof using the ZKP algorithm. Upon successful verifi-
cation, the university can confirm the students’ needs through the verifier, without the
need to disclose the confidential details of the students’ conditions each time. With this
structure, the university ensures that students receive the necessary support while main-
taining the confidentiality of their specific disabilities, striking a balance between neces-
sary verification and privacy.
Non‑interactive ZKPs forscalability
Non-Interactive Zero-Knowledge Proofs (NIZKPs) allow the prover to confirm shared
data with the verifier without the need for continuous back-and-forth exchanges
between the parties (Ben-Sasson etal., 2014). In contrast, Interactive Zero-Knowledge
Proofs (IZKPs) are achieved through an interactive exchange protocol, which involves a
sequence of challenge-response interactions between the verifier and the prover. Unlike
IZKPs, NIZKPs enable the prover to generate the proof in a single step, offering compu-
tational advantages. While Table2 summarizes the key differences between IZKPs and
NIZKPs, this discussion specifically focuses on NIZKPs.
A Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) is
a cryptographic technique that allows an individual to demonstrate the authenticity of a
claim without disclosing any detailed information about the claim and without any prior
communication between the involved parties (Groth, 2010). A zk-SNARK method must
satisfy the following four fundamental principles (ElSheikh & Youssef, 2023):
1. Perfect Completeness: For every valid statement paired with its corresponding valid
witness, an honest prover will always convince an honest verifier.
Fig. 3 Interactive workflow for student disability management using blockchain-based ZKPs
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Xu Smart Learning Environments (2024) 11:7
2. Computational Soundness: A malicious prover, even with significant computational
power, cannot convince the verifier of an incorrect statement.
3. Computational Zero-Knowledge: Even with an honestly generated proof, an adver-
sary cannot extract any information about the witness.
4. Succinctness: e proof generated is concise and the verification process is efficient,
running in polynomial time based on the security parameter.
e application of zk-SNARKs in a disability management system can be designed as
shown in Fig.4.
e system is designed to manage and support students with disabilities in a univer-
sity setting using zk-SNARKs and smart contracts. Initially, an administrator and eligi-
ble students participate in a setup ceremony to generate cryptographic keys for various
arithmetic circuits. e administrator then creates a list of eligible students and deploys
a smart contract, setting the stage for the subsequent phases. Students provide crypto-
graphic proof of their disability status to ensure that only eligible students can register
and benefit from the system. Following registration, students document their required
adjustments, which are cryptographically verified and stored securely on the blockchain.
After gathering all the adjustment documents, the administrator proceeds to the support
generation phase, where a comprehensive support plan is created based on the individ-
ual needs of the students. is plan is also cryptographically verified and stored on the
blockchain. is entire process ensures transparency, security, and privacy, allowing uni-
versities to cater to the needs of students with disabilities efficiently and confidentially.
Results
e results section focuses on two main areas: evaluating the cost-effectiveness of imple-
menting the ZKPs algorithm and assessing its impact on self-disclosure among students
with disabilities.
Algorithm implementation andcost analysis
We deployed a zk-SNARKs-based smart contract for managing students’ disabilities on
the Ethereum network and analyzed the associated transaction cost. Our open-source
Table 2 Comparative Analysis of IZKPs and NIZKPs
Feature/type IZKPs NIZKPs
Interactivity Involves back-and-forth communication
between the prover and verifier Relies on a single message from the prover to
the verifier without further interactions
Proof Generation Proof generation hinges on an interactive
exchange protocol Allows for one-step proof generation by the
prover
Process Characterized by a sequence of challenge-
response interactions Noted for offering computational advantages,
minimizing interactive overhead
Versatility Widely foundational, forming the basis for
many cryptographic protocols Often evolve from IZKPs via specific transfor-
mations
Security Founded on diverse cryptographic assump-
tions and predicated on both parties adher-
ing to the protocol during interaction
Built on certain assumptions, especially those
pertaining to the non-interactive transforma-
tion. Some may also depend on trusted setups
Efficiency Can be less efficient due to potential multi-
ple rounds of communication Highly efficient in contexts where ongoing
interaction is impractical or resource intensive
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Xu Smart Learning Environments (2024) 11:7
prototype provides a practical demonstration of the zk-SNARKs application. We meas-
ured the contract’s execution cost at 660,478 gas units, indicating the computational
effort required to process and integrate the transaction into the blockchain. Moreover,
the on-chain verification transaction, which assures the validity and integrity of the zk-
SNARKs proofs, consumed 1,195,474 gas units. It is noted that as the user base grows,
we anticipate a linear increase in transaction costs, highlighting the scalable nature of
the verification process.
We further assessed this technology’s real-world feasibility by reviewing the costs
associated with supporting individuals with disabilities in higher education settings.
According to Pitman etal. (2022), the Australian government allocated approximately
7.6 million AUD to the Disability Support Fund (DSF) in 2019. A significant portion of
this fund, 84.7%, was designated for the Additional Support for Students with Disabili-
ties (ASSD), which was primarily dedicated to reimbursing the costs incurred in pro-
viding educational support and equipment for students with disabilities. From 2015 to
2019, universities claimed an annual average of 11,017,589 AUD for disability support,
with only 58% typically reimbursed. Despite government assistance for an average of
3718 students per year, claims from educational institutions have outpaced the support
Initialize:
-Create an empty list of students: `studentList = []`
-Create an empty list of accommodations: `accommodationList = []`
Function ADD_STUDENT(studentID, studentName, disabilityProof):
-Create a new student object: `student = {ID: studentID, Name: studentName, Proof:
disabilityProof, Accommodations: []}`
-Add student to `studentList`
Function ADD_ACCOMMODATION(accommodationName, description):
-Create a new accommodation object: `accommodation = {Name: accommodationName,
Description: description}`
-Add accommodation to `accommodationList`
Function PROVE_DISABILITY(studentID, zkProof):
-Find student with `studentID` in `studentList`
-If zkProof validates the student's disability without revealing specifics:
-Mark student as verified
Function ASSIGN_ACCOMMODATION(studentID, accommodationName):
-Find verified student with `studentID` in `studentList`
-If student found:
-Find accommodation with `accommodationName` in `accommodationList`
-If accommodation found:
-Add accommodation to student's Accommodations list
Pre-defined zk-SNARK Functions:
-Mux(s, P, Q): Returns P if selector s=0, and Q if s=1
-LessThan(a, b): Returns 1 if a<b, and 0 otherwise
-GreaterThan(a, b): Returns 1 if a>b, and 0 otherwise
-CompC(a, c): Returns 1 if a>c, and 0 otherwise (where c is a constant)
-Bits2Num(a0,...,ak-1): Returns integer number represented by bits a0,...,ak-1
-IsPoint(x, y): Returns 1 if the pair (x, y) is a point on the elliptic curve, and 0 otherwise
-IsEqual(P, Q): Returns 1 if the twopoints P and Q are equal, and 0 otherwise
-eADD(P, Q): Point addition (P+Q) on the elliptic curve
-eSUB(P, Q): Point subtraction (P-Q) on the elliptic curve
-eScalarMUL(a, P): Scalar multiplication (aP) on the elliptic curve
Fig. 4 Disability management system pseudo-algorithm with zk-SNARKs
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Xu Smart Learning Environments (2024) 11:7
provided, averaging 1694 AUD in assistance per student each year. Recurrent costs,
which include a wide range of operational expenses, account for 58% of the total expen-
ditures, as detailed in Table3.
In our cost analysis of the practical application of zk-SNARKs for managing recurrent
expenses in disability support, the smart contract execution on the Ethereum network
required 660,478 gas units. With the Ethereum price in September 2023 at 2518 AUD
and a gas price of 7 gwei, this equates to a cost of approximately 11.64 AUD. Our analy-
sis contrasts this transaction cost with the traditional disability support funding model,
where the government’s ASSD per-student aid is about 1694 AUD. e transaction cost
for using the zk-SNARKs-based system represents roughly 0.6% of the ASSD per-stu-
dent support.
Impact ofZKPs onstudent disclosure
Challenges in assessing the impact of ZKPs on students with disabilities arise from their
general reluctance to disclose personal information. Among a cohort of 48,000 students
with disabilities in Australia, only 253 responded to the survey conducted by Clark etal.
(2018), indicating a response rate of less than 0.5%. is low participation highlights the
considerable hesitancy among students with disabilities to share their personal infor-
mation. To address this, our research uses hypothetical case studies to explore how the
implementation of ZKPs might facilitate the disclosure process for students with dis-
abilities, potentially leading to improved academic outcomes.
Clark etal. (2018) surveyed students with disabilities, asking them to rate their agree-
ment with various key statements related to self-disclosure on a scale from one (strong
disagreement) to five (strong agreement). eir analysis identified ’Disclosure benefits
students’ and ’Trust in the university’ are positively associated with the likelihood of dis-
closure, whereas ’Fear of prejudice at university’ and ’Data confidentiality concerns’ were
negatively associated. ese significant variables, their logistic regression estimates and
the average student ratings are presented in Table4.
Building upon these insights, this study presents hypothetical case studies based on
individual student profiles. We explore two distinct student groups in these case studies.
Group A, which has shown hesitance in disclosing their disability status due to confi-
dentiality concerns, often gives ’Data confidentiality concerns’ a high rating of 4 out of
5. With the ZKP algorithm implementation, the disability status of these students can be
verified without revealing specific details, potentially increasing their rate of disclosure
and willingness to access university support services. For instance, if Group A’s initial
likelihood of disclosure is 20% with high confidentiality concerns, the implementation
of ZKPs could reduce this rating to 2. According to logistic regression estimates, such a
Table 3 Annual expenditures allocation for disability support services
Expenditures Percentage Description
Recurrent 58 Salaries, software, hardware, training, projects, community outreach, consultancy,
memberships, maintenance, exams
Non-recurrent 31 Unpredictable expenses like case-by-case adjustments, specialist equipment
Indirect 11 Curriculum design, staff training, infrastructure, tech, space allocation in libraries,
web design
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Xu Smart Learning Environments (2024) 11:7
reduction could enhance their odds of disclosing by 7.4 times, corresponding to a 65%
probability—a significant improvement.
Group B, while recognizing the benefits of disclosure, is inhibited by the fear of stig-
matization, often gives ’Fear of prejudice at university’ a rating of 4. e introduction
of ZKPs may change this dynamic, allowing these students to securely disclose their
accommodation needs without the risk of exposing sensitive details. If Group B’s initial
disclosure rate is 50% with high fear of prejudice, reducing this rating to 2 could increase
their odds by a factor of 16, resulting in a disclosure probability of 89%.
Discussions
is section outlines the prospective benefits of integrating blockchain-powered tech-
nology in education to support inclusive education and describes how the application of
ZKP scan enhance the self-disclosure process for students with disabilities, contributing
to their academic success and construction of their digital identity.
Blockchain‑powered technology tosupport inclusive education
As we delve into transformative potential of blockchain technology in education, it is
important to consider the cost and efficiency of implementing these systems on a larger
scale. One of the challenges in employing zk-SNARKs-based smart contracts, especially
on the Ethereum, is managing the variability of transaction costs. is variability could
become significant when managing a large number of students with disabilities. For
instance, domestic undergraduate enrollments with disabilities have steadily increased
in Australia. e figure grew from 4.8% of total undergraduate students in 2011 to 9.4%
in 2021, indicating an increase from 42,000 to over 100,000 more students (Australian
Disability Clearinghouse on Education & Training, 2022).
To address this challenge, a promising approach is to shift zk-SNARK verification pro-
cesses off-chain. Moving the verification off-chain can significantly reduce transaction
costs and alleviate congestion on the Ethereum network, enhancing overall system effi-
ciency. is strategy leverages more efficient computational resources and circumvents
the high costs and storage limitations inherent in Ethereum’s blockchain. Ensuring the
off-chain verification processes to maintain the same level of security and reliability as
on-chain verifications is also important. Strategies such as distributing the verifying key
across multiple students or accommodation requests can further optimize the system.
Table 4 Significant variables influencing disclosure and average student ratings
Variable Logistic regression estimate Average
student
rating
Disclosure benefits students 2.86 4
Trust in university 1.72 3.9
Fear prejudice at university − 1.39 3.1
Data confidentiality concerns − 1 2.8
University does not need the information − 1.92 2.4
Do not know why should disclose − 2.33 2
Do not know how to disclose − 1.5 2
Academic GPA 0.08 N/A
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Xu Smart Learning Environments (2024) 11:7
e integration of the Ethereum network with file-sharing technologies like IPFS can
democratize access and reduce reliance on traditional infrastructures, such as central-
ized universities and physical resources.
Aligning with UN SDG 4, which emphasizes the importance of inclusive education,
blockchain technology can make higher education more accessible. As Kwok and Trei-
blmaier (2022) noted, reducing the transaction costs through blockchain can make
higher education more reachable and potentially address broader issues such as poverty
and social inequality. Advancements in blockchain technology can contribute to dis-
mantling barriers within the educational system, addressing broader societal challenges.
Importance ofprivacy forstudents withdisabilities
In the rapidly evolving technological landscape, where social interactions and com-
munity building predominantly occur online, privacy emerges as a critical concern,
especially for students with disabilities. e post-COVID-19 era has accelerated tech-
nological integration in education, blurring the boundaries between Learning Manage-
ment Systems and social media platforms. is shift raises concerns about data privacy
as noted by Kumi‐Yeboah etal. (2023). Students, particularly those from diverse back-
grounds, often grapple with balancing technology use and maintaining control over their
private information. Students with disabilities may face additional dilemmas, hesitating
to use online services specifically designed for their needs due to privacy concerns. De
Cesarei and Baldaro (2015) found that many such students exhibited significant reserva-
tions regarding their identity privacy when participating in online research. is cau-
tious approach reflects a broader trend among the youth who, as Muñoz-Rodríguez etal.
(2022) point out, adopt various methods to manage their online presence and safeguard
their privacy.
ZKPs emerge as a promising technological solution to these privacy concerns. By
facilitating the verification of information without revealing underlying data, ZKPs can
significantly enhance privacy in digital communities. is technology empowers stu-
dents with disabilities to engage in blockchain-powered communities with confidence,
knowing their disability status or personal information remains confidential. It creates
a secure environment for interaction, learning and growth without the fear of privacy
invasion. e integration of ZKPs in digital communities, particularly in educational set-
tings, marks a progressive step towards more inclusive and secure online spaces. While
ZKPs offer a pathway to more inclusive and secure digital interactions, it is important
for educational institutions and technology developers to navigate the ethical complexi-
ties and implement these solutions responsibly.
Impact ofdisclosure onacademic success forstudents withdisabilities
Students with disabilities have historically faced challenges in accessing quality, inclu-
sive education worldwide. For example, in the European Union, within the 30–34 age
group in 2019, only 33% of individuals with disabilities completed tertiary or equiva-
lent education, compared to 44% of those without disabilities (Commission etal., 2021).
Similarly, in the United States, the disability rate is higher among individuals with lower
educational levels, and educational attainment is closely linked to lower employment
percentages for persons with disabilities (McFarland etal., 2017). In Australia, data from
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Xu Smart Learning Environments (2024) 11:7
the Australian Disability Clearinghouse on Education and Training (2022) shows that
students with disabilities typically have lower success and graduation rates compared
to their non-disabled peers. Only 17% of students with disabilities aged over 20 hold a
bachelor’s degree or higher, compared to 35% of those without disabilities. In 2021, the
success rate, defined as the pass ratio of all attempted courses, was 80.7% for students
with disabilities, significantly lower than the 87.1% for those without disabilities. e
notable disadvantage in educational outcomes for students with disabilities underscores
the need for enhanced strategies to ensure equitable access and success for all students.
Our results section has demonstrated the potential effectiveness of adopting ZKPs
to enhance self-disclosure, thereby increasing support for students with disabilities
when needed. e ZKP algorithm, as a novel technology ensuring confidentiality,
could also alleviate concerns related to general discrimination, distrust of universi-
ties, and negative experiences with previous disclosures, as highlighted by Clark etal.
(2018). Furthermore, Clark etal. (2018) identified a strong correlation between a stu-
dent’s GPA and their propensity to disclose, suggesting that disclosure might not only
lead to academic support and enhancing performance, but also that students with
higher GPAs may be more inclined to disclose, perceiving an academic advantage.
Considering that academic GPAs are strong predictors of graduation rates, as sug-
gested by Denning etal. (2022), maintaining good grades can have a causal effect on
graduation and act as an indicator of learning ability. erefore, enhancing disclosure
rates could lead to a significant increase in academic GPAs, subsequently boosting the
academic performance and outcomes for students with disabilities. Greater academic
success among students with disabilities aligns with the aim of designing accessible
and inclusive education, facilitating broader civic participation, employment oppor-
tunities and community life, as advocated by the UN SDG 4.
Limitations
ZKPs offer a transformative approach to privacy protection and secure authentica-
tion, but the implementation is not without challenges. is study recognizes these
limitations, particularly the computational complexities of ZKPs and the challenges
in interactional dynamics, given their prototype status and the absence of real-person
interaction data for comprehensive evaluation.
Technological challenges withZKPs
X. Sun etal. (2021a) highlighted several challenges associated with ZKPs:
1. Standardization issues: e diversity of ZKP models hinders universal adoption. As
such, specific scenarios necessitate tailored applications.
2. Computational demands: ZKPs involve intricate mathematical computations. Cer-
tain models, especially zk-SNARKs in Zerocash, are computationally intensive and
rely on third parties for setup.
3. Dependence on trusted setups: Some ZKP methodologies require a trusted estab-
lishment phase, underscoring the pivotal role of trusted entities.
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Xu Smart Learning Environments (2024) 11:7
Emerging technologies, such as Secure Multi-Party Computation (SMPC) and Differ-
ential Privacy, present advanced cryptographic solutions for secure data transformations
and could supplement or even surpass the capabilities of ZKPs in certain contexts.
Interactional limitations
e integration of innovative technologies such as ZKPs often presents adoption chal-
lenges for various stakeholders, including students with disabilities, university support
staff, registered service providers, and potentially caregivers. Since this technology is still
at a prototype stage, its real-world adoption and effectiveness for students with disabili-
ties have yet to be thoroughly tested.
For stakeholders to fully understand and utilize ZKPs, comprehensive training may be
required for university staff and students. Chen etal. (2018) emphasized the potential
pitfalls of applying blockchain technology in educational settings. e complexity can
complicate the evaluation of subjective learning behaviors and outcomes such as essays
and classroom presentations. Additionally, integrating students’ educational data into
blockchain ledgers presents another challenge. While blockchain’s immutable nature
bolsters data security, it simultaneously imposes limitations on modifying educational
records, even when these changes may be justified.
Furthermore, numerous technical issues remain to be addressed for the effective appli-
cation of blockchain in education. ese issues include scalability, network congestion
management, and the development of user-friendly interfaces that accommodate the
diverse needs of all students, including those with disabilities. Addressing these chal-
lenges is crucial for the seamless adoption of blockchain and ZKPs in educational set-
tings, which ensures that the benefits of these technologies are fully realized without
compromising the learning experience or the rights of students.
Conclusions
In alignment with the UN Sustainable Development Goal 4, which emphasizes the
importance of inclusive education, this paper explores the potential of integrating ZKPs
via blockchain in the educational setting, particularly through blockchain technology.
is approach provides an innovative approach of supporting students with disabilities,
ensuring their privacy while enhancing administrative efficiency.
Our implementation of the zk-SNARK technology demonstrates how this advanced
cryptographic solution can streamline accommodation verification processes for stu-
dents with disabilities in higher education institutions. e disability management sys-
tem not only significantly improves operational efficiencies but also ensures an enhanced
self-disclosure process, providing robust support for the students. Our comparative cost
analysis highlights the potential economic advantages of blockchain-backed ZKP sys-
tems over traditional disability support structures. Additionally, our hypothetical case
studies suggest the disclosure rates would increase, presenting a promising strategy for
managing student disability needs more effectively.
We further discuss the technological benefits from a cost–benefit perspective, high-
lighting how blockchain technology can promote inclusive education, enhance privacy
to help establish individual identity in the digital era, and contribute to academic suc-
cess for students through increased disclosure. Privacy is a crucial element of individual
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Page 14 of 16
Xu Smart Learning Environments (2024) 11:7
identity in the digital age, especially for students with disabilities, who have long been
disadvantaged in academic success and employment opportunities due to hesitancy in
seeking help and concerns about confidentiality. Striking a balance between safeguarding
student privacy and ensuring appropriate accommodations can be challenging; however,
blockchain-integrated ZKP technology offers a promising solution. Beyond its technical
innovations, ZKPs provide a means for students to protect sensitive information, ensur-
ing it is not disclosed to unnecessary parties. e enhanced disclosure process facili-
tated by ZKPs can better address students’ accommodation needs, helping them achieve
greater academic success.
In conclusion, this study underscores the critical importance of embracing innovative
technologies like ZKPs and zk-SNARKs. e ultimate shared goal is to the create sus-
tainable, inclusive, and progressive educational environments. As blockchain and cryp-
tography technologies continue to evolve, the apparent benefits will increasingly drive
significant transformations in higher education. is research not only highlights these
technologies’ potential but also advocates for their strategic implementation to ensure
equitable access and success for all students, particularly those with disabilities.
Abbreviations
ASSD Additional support for students with disabilities
DSF Disability support fund
ETH Ethereum (a blockchain platform and cryptocurrency)
ICT Information and Communication Technology
IPFS InterPlanetary file system
IZKP Interactive zero-knowledge proof
NIZKP Non-interactive zero-knowledge proof
P2P Peer-to-peer
SDG Sustainable development goals
SMPC Secure multi-party computation
UN United Nations
zk-SNARK Zero-knowledge succinct non-interactive argument of knowledge
ZKP Zero-knowledge proof
Acknowledgements
Not applicable.
Author contributions
All aspects of the manuscript, including data analysis, interpretation, histological examinations, writing, and final
approval, were carried out by XX.
Funding
Not applicable.
Availability of data and materials
All supplementary materials, including the prototype algorithm code, are available in the ’ZKP’ directory of the reposi-
tory: [LINK REMOVED FOR BLINDED REVIEW ].
Declarations
Competing interests
The authors declare that they have no competing interests.
Received: 28 September 2023 Accepted: 1 February 2024
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