Private Information Retrieval Research Articles

Reexamination of the realtime protection for user privacy in practical quantum private query

Abstract Quantum private query (QPQ) is the quantum version for symmetrically private information retrieval. However, the user privacy in QPQ is generally guarded in the delayed and cheat-sensitive way. That is, the dishonest database holder Bob's cheating to elicit user privacy can only be discovered after the protocol is finished (when the user finds some errors in the retrieved database item). Such delayed detection may cause very unpleasant results for the user Alice in real-life applications. Without the realtime conscious of privacy leaking, Alice may make faulty decisions according to the received faulty item and suffer great loss. Worse yet, it is difficult for Alice to accuse a dishonest database to any one else though she can detect the cheating. Current efforts to protect user privacy in realtime in existing QPQ protocols mainly use two techniques, i.e., adding an honesty checking on the database or allowing the user to reorder the qubits. We reexamine these two kinds of QPQ protocols and find neither of them can work very well. We give concrete cheating strategies for both participants and show that honesty checking of inner participant should be dealt more carefully in for example the choosing of checking qubits. Finally, we give an idea to realize realtime detection of dishonest database in QPQ of blocks (i.e., multi-bit database items).

Privacy preserving large language models: ChatGPT case study based vision and framework

AbstractThe generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy‐preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre‐processing and preserving private context and the private training process for large‐scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy; specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and resource consumption. It is concluded that differential privacy, randomisation, and obfuscation can impact the training models' utility and performance; conversely, using Tor, Blockchain, and Private Information Retrieval (PIR) may introduce additional computational complexity and high training latency. It is believed that the proposed model could be used as a benchmark for privacy‐preserving LLMs for generative AI tools.

Prior entanglement exponentially improves one-server quantum private information retrieval for quantum messages

Quantum private information retrieval (QPIR) for quantum messages is a quantum communication task, in which a user retrieves one of the multiple quantum states from the server without revealing which state is retrieved. In the one-server setting, we find an exponential gap in the communication complexities between the presence and absence of prior entanglement in this problem with the one-server setting. To achieve this aim, as the first step, we prove that the trivial solution of downloading all messages is optimal under QPIR for quantum messages, which is a similar result to that of classical PIR but different from QPIR for classical messages. As the second step, we propose an efficient one-server one-round QPIR protocol with prior entanglement by constructing a reduction from a QPIR protocol for classical messages to a QPIR protocol for quantum messages in the presence of prior entanglement.

New private information retrieval codes for imperfect privacy

In this paper, we introduce new codes for the private information retrieval (PIR). The conventional Tian-Sun-Chen (TSC) scheme achieves PIR capacity under the perfect privacy conditions with the minimum message length. Based on the TSC scheme, weakly PIR (WPIR) schemes have been developed to benefit download costs by relaxing privacy. We show that the proposed scheme has lower download cost than the TSC scheme in the weak privacy setting. While our scheme is not capacity-achieving under perfect privacy, it has smaller message sizes compared with the TSC scheme.

Private information retrieval from locally repairable databases with colluding servers

We consider information-theoretical private information retrieval (PIR) from a coded database with colluding servers. We target, for the first time, locally repairable storage codes (LRCs). We consider any number of local groups g, locality r, local distance δ and dimension k. Our main contribution is a PIR scheme for maximally recoverable (MR) LRCs based on linearized Reed–Solomon codes, which achieve the smallest field sizes among MR-LRCs for many parameter regimes. In our scheme, nodes are identified with codeword symbols and servers are identified with local groups of nodes. Only locally non-redundant information is downloaded from each server, that is, only r nodes (out of r+δ−1) are downloaded per server. The PIR scheme achieves the (download) rate R=(N−k−rt+1)/N, where N=gr is the length of the MDS code obtained after removing the local parities, and for any t colluding servers such that k+rt≤N. For an unbounded number of stored files, the obtained rate is strictly larger than those of known PIR schemes that work for any MDS code. Finally, the obtained PIR scheme can also be adapted when communication between the user and each server is performed via linear network coding, achieving the same rate as previous PIR schemes for this scenario but with polynomial finite field sizes, instead of exponential. Our rates are equal to those of PIR schemes for Reed–Solomon codes, but Reed–Solomon codes are incompatible with the MR-LRC property or linear network coding, thus our PIR scheme is less restrictive in its applications.

Deceptive Information Retrieval.

We introduce the problem of deceptive information retrieval (DIR), in which a user wishes to download a required file out of multiple independent files stored in a system of databases while deceiving the databases by making the databases' predictions on the user-required file index incorrect with high probability. Conceptually, DIR is an extension of private information retrieval (PIR). In PIR, a user downloads a required file without revealing its index to any of the databases. The metric of deception is defined as the probability of error of databases' prediction on the user-required file, minus the corresponding probability of error in PIR. The problem is defined on time-sensitive data that keep updating from time to time. In the proposed scheme, the user deceives the databases by sending real queries to download the required file at the time of the requirement and dummy queries at multiple distinct future time instances to manipulate the probabilities of sending each query for each file requirement, using which the databases' make the predictions on the user-required file index. The proposed DIR scheme is based on a capacity achieving probabilistic PIR scheme, and achieves rates lower than the PIR capacity due to the additional downloads made to deceive the databases. When the required level of deception is zero, the proposed scheme achieves the PIR capacity.

Low Communication-Cost PSI Protocol for Unbalanced Two-Party Private Sets

Two-party private set intersection (PSI) plays a pivotal role in secure two-party computation protocols. The communication cost in a PSI protocol is normally influenced by the sizes of the participating parties. However, for parties with unbalanced sets, the communication costs of existing protocols mainly depend on the size of the larger set, leading to high communication cost. In this paper, we propose a low communication-cost PSI protocol designed specifically for unbalanced two-party private sets, aiming to enhance the efficiency of communication. For each item in the smaller set, the receiver queries whether it belongs to the larger set, such that the communication cost depends solely on the smaller set. The queries are implemented by private information retrieval which is constructed with trapdoor hash function. Our investigation indicates that in each instance of invoking the trapdoor hash function, the receiver is required to transmit both a hash key and an encoding key to the sender, thus incurring significant communication cost. In order to address this concern, we propose the utilization of a seed hash key, a seed encoding key, and a Latin square. By employing these components, the sender can autonomously generate all the necessary hash keys and encoding keys, obviating the multiple transmissions of such keys. The proposed protocol is provably secure against a semihonest adversary under the Decisional Diffie–Hellman assumption. Through implementation demonstration, we showcase that when the sizes of the two sets are 28 and 214, the communication cost of our protocol is only 3.3% of the state-of-the-art protocol and under 100 Kbps bandwidth, we achieve 1.46x speedup compared to the state-of-the-art protocol. Our source code is available on GitHub: https://github.com/TAN-OpenLab/Unbanlanced-PSI.

Querying Twice to Achieve Information-Theoretic Verifiability in Private Information Retrieval

Querying Twice to Achieve Information-Theoretic Verifiability in Private Information Retrieval

Private Information Retrieval and Its Extensions: An Introduction, Open Problems, Future Directions

Private Information Retrieval and Its Extensions: An Introduction, Open Problems, Future Directions

Parallel private information retrieval protocol with index anonymity for untrusted databases

Private information retrieval (PIR) enables a client to search data from a single (or multiple) untrusted server, without revealing which entry was queried. PIR can be divided into two categories: information-theoretic PIR (IT-PIR) and computational PIR (CPIR). However, there is a deployment challenge with IT-PIR because the non-collusion assumption is difficult to implement in practice. Meanwhile, due to the fact that CPIR involves performing cryptographic operations on each element, its performance significantly decreases as the size and number of database entries increase. To overcome these problems, based on homomorphic encryption, this paper presents a parallel PIR (ParPIR) with index anonymity for untrusted databases, which uses the batch encoding to compress request size. Furthermore, our ParPIR eliminates computationally expensive ciphertext multiplication operations, which improves the response time. Moreover, our ParPIR packs multiple responses through rotation column operations on ciphertext, thereby reducing the response size. Compared to previous PIR protocols, the offline time in our ParPIR has almost 1∼63× improvement; the online time in our ParPIR decreases by at least 49.9∼98.9%. Meanwhile, although the request size of our ParPIR has a 0∼31× increase, the response size of our ParPIR has a 0∼4095× improvement.

Decoy-state quantum private query protocol with two-way communication

Quantum private query (QPQ) is a communication protocol established under the condition of mutual distrust between communication parties, which is used to solve the symmetrically private information retrieval problem in the quantum field. However, most of the existing QPQ protocols are based on the ideal light source. In practice, the multi-photon pulse generated by the transmitter will bring great security problems. We analyze the actual security of QPQ protocol in two-way communication, and find that database security and user privacy will be seriously threatened under multi-photon pulse. So we adopt the decoy state method to solve the actual security problem of QPQ protocol in view of the user side as the light source. The results show that the decoy state method is suitable for QPQ protocol of two-way communication, and can effectively defend against multi-photon pulse attacks.

HPRoP: Hierarchical Privacy-preserving Route Planning for Smart Cities

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points ( endpoint location privacy ) and the route itself ( route privacy ). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼ 25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8).

Unified Approach to Secret Sharing and Symmetric Private Information Retrieval With Colluding Servers in Quantum Systems

Unified Approach to Secret Sharing and Symmetric Private Information Retrieval With Colluding Servers in Quantum Systems

Privacy-Preserving Computation:A Comprehensive Survey of Methods and Applications

This paper presents a comprehensive review of privacy-preserving computation, including its various methods, such as Trusted Environment Execution (TEE) computation, Secure Multi-Party Computation (SMPC) , Federated Learning (FL) , Differential Privacy (DP) , and Private Information Retrieval (PIR) , et. It also analyzes and compares these methods from the aspects of security, advantages/disadvantages, and risks. Additionally, this paper investigates the applications and development of privacy-preserving computation, which finally demonstrates that privacy-preserving computation has a significant contribution on data circulation and data value realization. At last, the paper analyzes the current situation and challenges of privacy- preserving computation, while pointing out the future direction of it.

Unconventional seed-assisted strategy for Al-rich hierarchical ZSM-48 zeolite

Inferior diffusion capacity and insufficient acid density hinder the practical application of ZSM-48 zeolite. Finding a simple and practical strategy to simultaneously address these two defects remains a challenge. In response to this dilemma, we developed an unconventional seed-assisted synthesis strategy for Al-rich hierarchical ZSM-48 zeolite. This approach allows for achieving a broader range of silica to alumina ratio and accelerates the entire crystallization process through the selection of unconventional seeds. The synergy between the seed and organic template was demonstrated to play a pivotal role in facilitating nucleation. Direct evidence from 1H-29Si CP MAS NMR, TG, and IR results demonstrates that hexamethonium ions (HM2+) electrostatically adsorb at the defect sites on the seed, thereby promoting nucleation sites formation. Smaller seed crystals undergo more etching during the induction period, resulting in additional defects and enhanced nucleation ability. The obtained catalyst exhibits a diffusion time constant (Deff/L2) nine times that of conventional ZSM-48 zeolite when using p-xylene as a probe molecule. In m-xylene isomerization reaction, Al-rich hierarchical ZSM-48 demonstrates excellent stability along with higher selectivity and yield for p-xylene compared to typical ZSM-5 catalysts. Remarkably, long-term testing of 1000 h yields over 22.5 % of p-xylene, indicating the potential of this catalyst as an alternative for xylene isomerization reaction. This work not only advances the practical application process of ZSM-48 catalyst but also provides valuable insights for optimizing other zeolites.

On private information retrieval supporting range queries

On private information retrieval supporting range queries

Multi-User PIR with Cyclic Wraparound Multi-Access Caches.

We consider the problem of multi-access cache-aided multi-user Private Information Retrieval (MACAMuPIR) with cyclic wraparound cache access. In MACAMuPIR, several files are replicated across multiple servers. There are multiple users and multiple cache nodes. When the network is not congested, servers fill these cache nodes with the content of the files. During peak network traffic, each user accesses several cache nodes. Every user wants to retrieve one file from the servers but does not want the servers to know their demands. This paper proposes a private retrieval scheme for MACAMuPIR and characterizes the transmission cost for multi-access systems with cyclic wraparound cache access. We formalize privacy and correctness constraints and analyze transmission costs. The scheme outperforms the previously known dedicated cache setup, offering efficient and private retrieval. Results demonstrate the effectiveness of the multi-access approach. Our research contributes an efficient, privacy-preserving solution for multi-user PIR, advancing secure data retrieval from distributed servers.

Private Information Retrieval in Large Scale Public Data Repositories

The tutorial focuses on Private Information Retrieval (PIR), which allows clients to privately query public or server-owned databases without disclosing their queries. The tutorial covers the basic concepts of PIR such as its types, construction, and critical building blocks, including homomorphic encryption. It also discusses the performance of PIR, existing optimizations for scalability, real-life applications of PIR, and ways to extend its functionalities.

Information-theoretic privacy in federated submodel learning

We consider information-theoretic privacy in federated submodel learning, where a global server has multiple submodels. Compared to the privacy considered in the conventional federated submodel learning where secure aggregation is adopted for ensuring privacy, information-theoretic privacy provides the stronger protection on submodel selection by the local machine. We propose an achievable scheme that partially adopts the conventional private information retrieval (PIR) scheme that achieves the minimum amount of download. With respect to computation and communication overhead, we compare the achievable scheme with a naïve approach for federated submodel learning with information-theoretic privacy.

SPG: Structure-Private Graph Database via SqueezePIR

Many relational data in our daily life are represented as graphs, making graph application an important workload. Because of the large scale of graph datasets, moving graph data to the cloud becomes a popular option. To keep the confidential and private graph secure from an untrusted cloud server, many cryptographic techniques are leveraged to hide the content of the data. However, protecting only the data content is not enough for a graph database. Because the structural information of the graph can be revealed through the database accessing track. In this work, we study the graph neural network (GNN), an important graph workload to mine information from a graph database. We find that the server is able to infer which node is processing during the edge retrieving phase and also learn its neighbor indices during GNN's aggregation phase. This leads to the leakage of the information of graph structure data. In this work, we present SPG, a structure-private graph database with SqueezePIR. Our SPG is built on top of Private Information Retrieval (PIR), which securely hides which nodes/neighbors are accessed. In addition, we propose SqueezePIR, a compression technique to overcome the computation overhead of PIR. Based on our evaluation, our SqueezePIR achieves 11.85× speedup on average with less than 2% accuracy loss when compared to the state-of-the-art FastPIR protocol.