Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of the effective selection valve and the effective expectation of merit are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.
Riaz Ahmad Ziar, Syed Irfan Ullah, Rafiulllah Omar
The introduction of smart devices and the IOT network has led to the creation of large amounts of data that require protection from intrusion. Most users desire to have personal data kept confidential while seeking for platforms that would prohibit their vendors from distributing it to third parties without their consent. However, the users that are conscious of data privacy often share information with third parties, contradicting their intentions in keeping their information confidential. The difference between user intentions and actions regarding data privacy is called privacy paradox while privacy fatigue refers to the weariness of people on implementing security and privacy solutions. In this proposed system we design and develop smart contracts to provide interaction for the IoT device and company which require personal data. A company or Application requests personal information from the device to share the device sends, that information to the smart contract, smart contract uses dynamic rules to check PII in the users' personal information. Base on the PII(,) system would alert users on the limit and risk of sharing personal information through a public network. We used solidity programing language for the modeled of the smart contract. The performance of the contract is evaluated on the Repsten test network.
Nathaniel Aldred, Luke Baal, Graeham Broda, Steven Trumble · 5 authors
A blockchain is a distributed ledger forming a distributed consensus on a\nhistory of transactions. It is the underlying technology for the Bitcoin\ncryptocurrency, but there are many applications beyond the financial sector.\nWith built-in security and removal of the need for third party trust,\nblockchain has started to see some use within contract applications among other\nthings. In this paper, we present the design and implementation of a\npermissioned-based blockchain third party consent management system, whose\npolicy can be decided by a government agency. We have constructed a proof of\nconcept implementation using Hyperledger Fabric to provide a service that\nallows end-users to control and consent to who manages their private\ninformation. We believe our solution meets the guiding principles of EU General\nData Protection Regulation or GDPR. While our performance and usability\nevaluation are limited, our solution design and its implementation meet the 7\nfoundational principles of privacy by design.\n
We consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully- and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.
In fifth-generation (5G) networks and beyond, communication latency and network bandwidth will be no longer be bottlenecks to mobile users. Thus, almost every mobile device can participate in distributed learning. That is, the availability issue of distributed learning can be eliminated. However, model safety will become a challenge. This is because the distributed learning system is prone to suffering from byzantine attacks during the stages of updating model parameters and aggregating gradients among multiple learning participants. Therefore, to provide the byzantine-resilience for distributed learning in the 5G era, this article proposes a secure computing framework based on the sharding technique of blockchain, namely PiRATE. To prove the feasibility of the proposed PiRATE, we implemented a prototype. A case study shows how the proposed PiRATE contributes to distributed learning. Finally, we also envision some open issues and challenges based on the proposed byzantine- resilient learning framework.
This paper proposes SilentDelivery, a secure, scalable and cost-efficient protocol for implementing timed information delivery service in a decentralized blockchain network. SilentDelivery employs a novel combination of threshold secret sharing and decentralized smart contracts. The protocol maintains shares of the decryption key of the private information of an information sender using a group of mailmen recruited in a blockchain network before the specified future time-frame and restores the information to the information recipient at the required time-frame. To tackle the key challenges that limit the security and scalability of the protocol, SilentDelivery incorporates two novel countermeasure strategies. The first strategy, namely silent recruitment, enables a mailman to get recruited by a sender silently without the knowledge of any third party. The second strategy, namely dual-mode execution, makes the protocol run in a lightweight mode by default, where the cost of running smart contracts is significantly reduced. We rigorously analyze the security of SilentDelivery and implement the protocol over the Ethereum official test network. The results demonstrate that SilentDelivery is more secure and scalable compared to the state of the art and reduces the cost of running smart contracts by 85%.
Abstract Background With the advent of precision medicine, pharmacogenomics data is becoming increasingly critical to patient care. These data describe the relationship between a particular variant in the genome and the response to a drug by the patient. As utilizing this kind of data becomes more integral to medical treatment decisions, appropriate storage and sharing of this data will be critical. A potential way of securely storing and sharing pharmacogenomics data is a smart contract with the Ethereum blockchain. This is an open-source blockchain platform for decentralized applications. A transaction-based, state machine, the “world” of Ethereum maintains user accounts and storage in a network state. Immutable pieces of code called “smart contracts” may be deployed to the Ethereum network and run on the Ethereum Virtual Machine when called by a user or other contract. The 2019 iDASH (Integrating Data for Analysis, Anonymization, and Sharing) competition for Secure Genome Analysis challenged participants to develop time- and space-efficient smart contracts to log and query gene-drug relationship data on the Ethereum blockchain. Methods We designed a smart contract to store and query pharmacogenomics data (gene-drug interaction data) in Ethereum using an index-based, multi-mapping approach allowing for time and space efficient storage and query. Our solution to the IDASH competition ranked in the top three at a workshop held in Bloomington, IN in October 2019. Although our solution performed well in the challenge, we wanted to improve its scalability and query efficiency. To that end, we developed an alternate “fastQuery” solution that stores pooled rather than raw data, allowing for significantly improved query time for 0-AND queries, and constant query time for 1- and 2-AND queries. Results We tested the performance of both of our solutions in Truffle (v5.0.31) using datasets ranging from 100 to 1000 entries, and inserting data at 25, 50, 100, and 200 observations at a time. On a private, proof-of-authority test network, our challenge solution requires approximately 70 seconds, 500 MB of memory, and 80 MB of disk space to insert 1000 entries (200 at a time); and 400 ms and 5 MB of memory to query a two-AND query from 1000 entries. This solution exhibits constant memory for insertion and querying, and linear query time. Our alternate fastQuery solution requires approximately 60 seconds, 500 MB of memory, and 80 MB of disk space to insert 1000 entries (200 at a time); and 83 ms and 5 MB of memory to query a two-AND query from 1000 entries. This solution exhibits constant memory for insertion and querying, linear query time for 0-AND queries, and constant query time for 1- and 2-AND queries in a database of up to 1000 entries. Conclusion In this study we showed that pharmacogenomics data can be stored and queried efficiently on the Ethereum blockchain. Our approach has the potential to be useful for a wide range of datasets in biomedical research; while we focused on gene-drug interaction data, our solution designs could be used to store a range of clinical trial data. Moreover, our solutions could be adapted to store and query data in any field where high-integrity data storage and efficient access is required.
Benjamin S. Glicksberg, Shohei Burns, Robert Currie, A. Clark Griffin · 8 authors
BACKGROUND: Efficiently sharing health data produced during standard care could dramatically accelerate progress in cancer treatments, but various barriers make this difficult. Not sharing these data to ensure patient privacy is at the cost of little to no learning from real-world data produced during cancer care. Furthermore, recent research has demonstrated a willingness of patients with cancer to share their treatment experiences to fuel research, despite potential risks to privacy. OBJECTIVE: The objective of this study was to design, pilot, and release a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of deidentified clinical and genomic data with a focus on late-stage cancer. METHODS: We created and piloted a blockchain-authenticated system to enable secure sharing of deidentified patient data derived from standard of care imaging, genomic testing, and electronic health records (EHRs), called the Cancer Gene Trust (CGT). We prospectively consented and collected data for a pilot cohort (N=18), which we uploaded to the CGT. EHR data were extracted from both a hospital cancer registry and a common data model (CDM) format to identify optimal data extraction and dissemination practices. Specifically, we scored and compared the level of completeness between two EHR data extraction formats against the gold standard source documentation for patients with available data (n=17). RESULTS: Although the total completeness scores were greater for the registry reports than those for the CDM, this difference was not statistically significant. We did find that some specific data fields, such as histology site, were better captured using the registry reports, which can be used to improve the continually adapting CDM. In terms of the overall pilot study, we found that CGT enables rapid integration of real-world data of patients with cancer in a more clinically useful time frame. We also developed an open-source Web application to allow users to seamlessly search, browse, explore, and download CGT data. CONCLUSIONS: Our pilot demonstrates the willingness of patients with cancer to participate in data sharing and how blockchain-enabled structures can maintain relationships between individual data elements while preserving patient privacy, empowering findings by third-party researchers and clinicians. We demonstrate the feasibility of CGT as a framework to share health data trapped in silos to further cancer research. Further studies to optimize data representation, stream, and integrity are required.
Credential fraud is a widespread practice that undermines investment and confidence in higher education systems and bears significant economic and social costs. Legacy credential verification systems are typically time-consuming, costly, and bureaucratic, and struggle against certain classes of credential fraud. In this paper, we propose a comprehensive blockchain-based credential verification solution, Cerberus, which is considerably more efficient, easy and intuitive to use, and effectively mitigates widespread manifestations of credential fraud. Cerberus also improves significantly upon other blockchain-based solutions in the research literature: it adheres closely to the existing credential verification ecosystem, it addresses a threat model informed by real-world fraud scenarios. Moreover, Cerberus uses on-chain smart contracts for credential revocation, and it does not entail students or employers to manage digital identities or cryptographic credentials to use the system. We prototype our solution and describe our attempt to design an online verification service with a rich feature set, including data privacy, transcript verification, and selective disclosure of data. We hope this effort contributes positively to towards alleviating the problem of fake credentials.
With the digitization of traditional medical records, medical institutions encounter difficult problems, such as electronic health record storage and sharing. Patients and doctors spend considerable time querying the required data when accessing electronic health records, but the obtained data are not necessarily correct, and access is sometimes restricted. On this basis, this study proposes a medical data sharing scheme based on permissioned blockchains, which use ciphertext-based attribute encryption to ensure data confidentiality and access control of medical data. Under premise of ensuring patient identity privacy, a polynomial equation is used to achieve an arbitrary connection of keywords, and then blockchain technology is combined. In addition, the proposed scheme has keyword-indistinguishability against adaptive chosen keyword attacks under the random oracle model. Analysis shows that the scheme has high retrieval efficiency.
For the modern world where data is becoming one of the most valuable assets,\nrobust data privacy policies rooted in the fundamental infrastructure of\nnetworks and applications are becoming an even bigger necessity to secure\nsensitive user data. In due course with the ever-evolving nature of newer\nstatistical techniques infringing user privacy, machine learning models with\nalgorithms built with respect for user privacy can offer a dynamically adaptive\nsolution to preserve user privacy against the exponentially increasing\nmultidimensional relationships that datasets create. Using these privacy aware\nML Models at the core of a Federated Learning Ecosystem can enable the entire\nnetwork to learn from data in a decentralized manner. By harnessing the\never-increasing computational power of mobile devices, increasing network\nreliability and IoT devices revolutionizing the smart devices industry, and\ncombining it with a secure and scalable, global learning session backed by a\nblockchain network with the ability to ensure on-device privacy, we allow any\nInternet enabled device to participate and contribute data to a global privacy\npreserving, data sharing network with blockchain technology even allowing the\nnetwork to reward quality work. This network architecture can also be built on\ntop of existing blockchain networks like Ethereum and Hyperledger, this lets\neven small startups build enterprise ready decentralized solutions allowing\nanyone to learn from data across different departments of a company, all the\nway to thousands of devices participating in a global synchronized learning\nnetwork.\n
Muqaddas Naz, Fahad Ahmed Al-Zahrani, Rabiya Khalid, Nadeem Javaid · 7 authors
In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, the user is encouraged to register the reviews about data. These reviews are validated through Watson analyzer to filter out the fake reviews. The customers registering valid reviews are given incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption, and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner, and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real set-up. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows the least computational time as compared to advanced encryption standard (AES) 128 and 256.
Participatory sensing is gaining popularity as a method for collecting and sharing information from distributed local environments using sensor-rich mobile devices. There are a number of participatory sensing applications currently in wide use, such as location-based service applications (e.g., Waze navigation). Usually, these participatory applications collect tremendous amounts of sensing data containing personal information, including user identity and current location. Due to the high sensitivity of this information, participatory sensing applications need a privacy-preserving mechanism, such as anonymity, to secure and protect personal user data. However, using anonymous identifiers for sensing sources proves difficult when evaluating sensing data trustworthiness. From this perspective, a successful participatory sensing application must be designed to consider two challenges: (1) user privacy and (2) data trustworthiness. To date, a number of privacy-preserving reputation techniques have been proposed to satisfy both of these issues, but the protocols contain several critical drawbacks or are impractical in terms of implementation. In particular, there is no work that can transparently manage user reputation values while also tracing anonymous identities. In this work, we present a blockchain-based privacy-preserving reputation framework called BPRF to transparently manage user reputation values and provide a transparent tracing process for anonymous identities. The performance evaluation and security analysis show that our solution is both practical and able to satisfy the two requirements for user privacy and data trustworthiness.
Alevtina Dubovitskaya, Petr Novotny, Zhigang Xu, Fusheng Wang
BACKGROUND: Timely sharing of electronic health records across providers, while ensuring data security and privacy, is essential for prompt care of cancer patients, as well as for the development of medical research and the enhancement of personalized medicine. Yet, it is not trivial to achieve efficient consent management, data exchange, and access-control policy enforcement, in particular, in decentralized settings, and given the gravity of the condition such as cancer. Using blockchain technology (BCT) has been recently advocated by research communities and gained momentum from the industry perspective. However, most of the proposed solutions are at the level of a prototype, and blockchain-based healthcare data management systems are not in place yet. SUMMARY: This paper presents a systematic literature review, aiming to analyze the motivations, advantages, and limitations, as well as barriers and future challenges faced when applying the state-of-the-art distributed ledger technology in oncology. We then discuss its outcomes and propose the direction of the future research that can help to attain integration and adoption of the BCT for data-sharing, medical research, and the pharmaceutical supply chain in oncology, as well as in healthcare in general. Key Messages:BCT has the potential to enhance data-sharing (for primary care and medical research), as well as to attain optimization of the pharmaceutical supply chain by bringing properties such as transparency, traceability, and immutability to the applications. However, BCT itself cannot guarantee data privacy and security. Thus, it is never proposed as a stand-alone technology, but as a combined technology with cryptographic techniques. Regardless of the number of existing prototypes of blockchain-based healthcare systems, due to the existing barriers of the adoption (e.g., legal, social, and technological limitations), there is a lack of evaluation in real-world settings. Aiming to overcome these limitations, we propose future research directions that include design of the privacy-preserving hybrid data storage, interoperable infrastructures and architecture, and are compliant with the international laws and regulations.
Statistical analysis of health data is an essential task in healthcare. However, existing healthcare systems are incompatible with this critical need due to privacy restrictions. A recently emerged technology called Blockchain has shown great promise for mitigating this incompatibility. In this work, we aim to improve existing secure statistical analysis protocols by leveraging the blockchain technology. We propose a novel method that enables researchers to perform statistical analysis on health data in a privacy-preserving, secure, and precise manner.
If the cloud is just someone else's computer, securing forensic evidence in case of a breach can be tricky. A blockchain-based distributed ledger could contribute to solve this problem, provided the required forensic information finds its way onto the blockchain. In this paper we sketch how blockchain technology and smart contracts apply to various processing models and their respective forensic challenges, and highlight how an accountability-based approach will be instrumental to the overall acceptability of the solution.
With the arrival of the Internet of Things (IoT) era and the rise of Big Data, cloud computing, and similar technologies, data resources are becoming increasingly valuable. Organizations and users can perform all kinds of processing and analysis on the basis of massive IoT data, thus adding to their value. However, this is based on data-sharing transactions, and most existing work focuses on one aspect of data transactions, such as convenience, privacy protection, and auditing. In this paper, a data-sharing-transaction application based on blockchain technology is proposed, which comprehensively considers various types of performance, provides an efficient consistency mechanism, improves transaction verification, realizes high-performance concurrency, and has tamperproof functions. Experiments were designed to analyze the functions and storage of the proposed system.
We initiate the study of non-interactive zero-knowledge (NIZK) arguments for languages in QMA. Our first main result is the following: if Learning With Errors (LWE) is hard for quantum computers, then any language in QMA has an NIZK argument with preprocessing. The preprocessing in our argument system consists of (i) the generation of a CRS and (ii) a single (instance-independent) quantum message from verifier to prover. The instance-dependent phase of our argument system involves only a single classical message from prover to verifier. Importantly, verification in our protocol is entirely classical, and the verifier needs not have quantum memory; its only quantum actions are in the preprocessing phase. Our second contribution is to extend the notion of a classical proof of knowledge to the quantum setting. We introduce the notions of arguments and proofs of quantum knowledge (AoQK/PoQK), and we show that our non-interactive argument system satisfies the definition of an AoQK. In particular, we explicitly construct an extractor which can recover a quantum witness from any prover which is successful in our protocol. Finally, we show that any language in QMA has an (interactive) proof of quantum knowledge.
Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.
Distributed ledger technology offers numerous desirable attributes to\napplications in the enterprise context. However, with distributed data and\ndecentralized computation on a shared platform, privacy and confidentiality\nchallenges arise. Any design for an enterprise system needs to carefully cater\nfor use case specific privacy and confidentiality needs. With the goal to\nfacilitate the design of enterprise solutions, this paper aims to provide a\nguide to navigate and aid in decisions around common requirements and\nmechanisms that prevent the leakage of private and confidential information. To\nfurther contextualize key concepts, the design guide is then applied to three\nenterprise DLT protocols: Hyperledger Fabric, Corda, and Quorum.\n
Wendy Charles, Natalie Marler, Lauren Long, Sean T. Manion
As clinical research moves toward real-world data capture with increased data sharing, there is a growing need for patient-centered technologies that ensure data authenticity and promote researcher and patient access. Blockchain is one of an emerging set of distributed ledger technologies with the potential to offer both research data transparency and trust, while offering robust security measures. As blockchain-based systems are being developed for clinical research applications, these systems may be required to follow state and federal research regulations, such as ethical protections for human participants and data privacy. Blockchain developers and research organizations alike are struggling to identify and interpret these regulatory requirements. Further, regulatory agencies and policymakers have not yet provided blockchain stakeholders with clear guidelines to achieve compliance. This article provides an introduction to the clinical research and health information privacy regulations in the United States as well as data design standards and electronic signature laws. We also offer recommendations for blockchain developers, researchers, and research organizations for achieving compliant blockchain solutions in clinical research.
Shantanu Pal, Tahiry Rabehaja, Ambrose Hill, Michael Hitchens · 5 authors
With the advancement of the Internet of Things (IoT) in recent years, there is a bigger potential to use online services than ever before. The use of the IoT brings numerous opportunities for both service providers and end users, however, it faces critical questions of security and privacy. Toward this, access control is one of the significant security challenges for the IoT, in particular, considering the characteristics of such IoT systems. To develop a secure access control architecture for the IoT, the propagation of access right delegation is a major issue. Many proposals present access control issues for the IoT but given the specific context of access right delegation, it is still in its infancy. This article presents an approach to address such a delegation issue for the IoT using the blockchain technology. We propose a delegation model that employv the critical issues, e.g., the use of nonunique identities, asynchronous and flexible delegation nature of communication for the IoT without the need of a centralized system. The goal of our primitive is to use attributes for validating the identity of an entity instead of relying on a concrete unique identity of an entity. To provide privacy for the attributes, we propose a dual blockchain architecture that moves the attribute storage and access of the public blockchain and onto a secure private blockchain. To demonstrate the feasibility of our proposed approach, we evaluate the system performances using the Ethereum blockchain network.
Wouter Lueks, Bogdan Kulynych, Jules Fasquelle, Simon Le Bail-Collet · 5 authors
Zero-knowledge proofs are an essential building block in many privacy-preserving systems. However, implementing these proofs is tedious and error-prone. In this paper, we present zksk, a well-documented Python library for defining and computing sigma protocols: the most popular class of zero-knowledge proofs. In zksk, proofs compose: programmers can convert smaller proofs into building blocks that then can be combined into bigger proofs. zksk features a modern Python-based domain-specific language. This makes possible to define proofs without learning a new custom language, and to benefit from the rich Python syntax and ecosystem. The library is available at https://github.com/spring-epfl/zksk
Organized surveillance, especially by governments poses a major challenge to individual privacy, due to the resources governments have at their disposal, and the possibility of overreach. Given the impact of invasive monitoring, in most democratic countries, government surveillance is, in theory, monitored and subject to public oversight to guard against violations. In practice, there is a difficult fine balance between safeguarding individual's privacy rights and not diluting the efficacy of national security investigations, as exemplified by reports on government surveillance programs that have caused public controversy, and have been challenged by civil and privacy rights organizations. Surveillance is generally conducted through a mechanism where federal agencies obtain a warrant from a federal or state judge (e.g., the US FISA court, Supreme Court in Canada) to subpoena a company or service-provider (e.g., Google, Microsoft) for their customers' data. The courts provide annual statistics on the requests (accepted, rejected), while the companies provide annual transparency reports for public auditing. However, in practice, the statistical information provided by the courts and companies is at a very high level, generic, is released after-the-fact, and is inadequate for auditing the operations. Often this is attributed to the lack of scalable mechanisms for reporting and transparent auditing. In this paper, we present SAMPL, a novel auditing framework which leverages cryptographic mechanisms, such as zero knowledge proofs, Pedersen commitments, Merkle trees, and public ledgers to create a scalable mechanism for auditing electronic surveillance processes involving multiple actors. SAMPL is the first framework that can identify the actors (e.g., agencies and companies) that violate the purview of the court orders. We experimentally demonstrate the scalability for SAMPL for handling concurrent monitoring processes without undermining their secrecy and auditability.