Information Retrieval Problem Research Articles

Software requirement specific entity extraction using transformer models

Software Requirement Specifications (SRS) documents provide the description of requirements and expectations attributed to software products. The structured text present in the SRS documents serves as a guide for developers in defining various functions in the process of software development. Software specific entity extraction is an important pre-processing step for various Natural Language Processing (NLP) tasks in the requirement engineering domain such as entity-centric search systems, SRS document summmarization, requirement classification, and requirement quality management. Recent advances in transformer-based models have significantly contributed to NLP and information retrieval problems, and achieved state-of-the-art performance for domain specific entity extraction tasks. In this study, we employ the transformer models including BERT, RoBERTa and ALBERT for software specific entity extraction. For this purpose, we annotate three requirement datasets, namely, DOORS, SRE, and RQA with varied sets of software specific entities. Our numerical study shows that transformer models are able to outperform the traditional approaches such as ML-CRF, and we find that BERT variants improve the F1-scores by 4% and 5% on the DOORS and SRE datasets, respectively. We conduct entity level error analysis to examine the partial and exact matching of entities and respective boundaries. Lastly, we experiment with few-shot learning to create sample efficient NER systems with template-based BART model

Learning to transfer knowledge from RDF Graphs with gated recurrent units

The Internet is a vital part of today’s ecosystem. The speedy evolution of the Internet has brought up practical issues such as the problem of information retrieval. Several methods have been proposed to solve this issue. Such approaches retrieve the information by using SPARQL queries over the Resource Description Framework (RDF) content which requires a precise match concerning the query structure and the RDF content. In this work, we propose a transfer learning-based neural learning method that helps to search RDF graphs to provide probabilistic reasoning between the queries and their results. The problem is formulated as a classification task where RDF graphs are preprocessed to abstract the N-Triples, then encode the abstracted N-triples into a transitional state that is suitable for neural transfer learning. Next, we fine-tune the neural learner to learn the semantic relationships between the N-triples. To validate the proposed approach, we employ ten-fold cross-validation. The results have shown that the anticipated approach is accurate by acquiring the average accuracy, recall, precision, and f-measure. The achieved scores are 97.52%, 96.31%, 98.45%, and 97.37%, respectively, and outperforms the baseline approaches.

A design approach for a distributed system to handle Chinese

In designing systems to handle non-Roman alphabet languages, unique problems in information retrieval are encountered. The concept of "P-trees” will be explained and compared to B-trees. Experiences in applying P-trees to non-Roman alphabet retrieval environments will be covered in detail.

Intermittent Private Information Retrieval With Application to Location Privacy

We study the problem of intermittent private information retrieval with multiple servers, in which a user consecutively requests one of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> messages from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> replicated databases such that part of requests need to be protected while others do not need privacy. Motivated by the location privacy application, the correlation between requests is modeled by a Markov chain. We propose an intermittent private information retrieval scheme that concatenates an obfuscation scheme and a private information retrieval scheme for the time period when privacy is not needed, to prevent leakage incurred by the correlation over time. In the end, we illustrate how the proposed scheme for the problem of intermittent private information retrieval with Markov structure correlation can be applied to design a location privacy protection mechanism in the location privacy problem.

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification.

Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.

The Capacity of Symmetric Private Information Retrieval Under Arbitrary Collusion and Eavesdropping Patterns

We study the symmetric private information retrieval (SPIR) problem under arbitrary collusion and eavesdropping patterns for replicated databases. We find its capacity, which is the same as the capacity of the original SPIR problem with the number of servers <i>N</i> replaced by a number <i>F</i>*. The number <i>F</i>* is the optimal solution to a linear programming problem, and it is a function of the joint pattern, which is the union of the collusion and eavesdropping pattern. This is the first result that shows how two arbitrary patterns collectively affect the capacity of the SPIR problem. We draw the conclusion that for SPIR problems, the collusion and eavesdropping constraints are interchangeable in terms of capacity, i.e., the two patterns play the same role in the SPIR problem and the capacity remains unchanged if we exchange the colluding and eavesdropping patterns. As corollaries of our result, the capacity of the SPIR problem under arbitrary collusion patterns, and the capacity of the PIR problem where each colluding set is included in some eavesdropping set, are also found. Some extensions with restrictions to finite message lengths are provided, and in this case, upper and lower bounds on the capacity are given. The lower bound is described with a solution to an integer linear programming problem.

Building Capacity-Achieving T-PIR Schemes for Some Parameters Over Binary Field via Subfield Sub-Codes

The T-Private Information Retrieval (T-PIR) problem is that a user wishes to retrieve a single record from N servers, without revealing the identity of the desired record even if the number of colluding servers up to T. Here every server stores all M records and each record can be viewed as a vector over the q-ary finite field Fq. To reduce the field size, a capacity-achieving T-PIR scheme based on some MDS array codes for all possible N > T,M ≥ 2 was constructed by Xu and Zhang in 2019. A key idea in that scheme is to construct some MDS array codes in query phase, whose generator matrices satisfy the recovery property (see (C1) in Example 1, page 6), i.e., there exist some specific columns in each generator matrix such that the number of them is equal to the rank of such matrix. We call the set of each column index of those columns in each generator matrix as the column index set. However, that scheme didn’t give an explicit selection of the column index set satisfying the recovery property. In this paper, let N = d(2t - 1), N > T = dt > 1,M ≥ 3 and d ≥ 1, under the constraint of 2t-1 ≥ N, we present a novel method to construct capacity-achieving T-PIR schemes over the binary field with an explicit selection of the column index set. Moreover, our scheme has sub-packetization N(2t-1)M-2, which is optimal for all linear capacity-achieving T-PIR schemes determined in [38]. The key to our method is that all locators and column multipliers are precisely selected such that a Generalized Reed-Solomon (GRS) code defined by them has a zero-dimensional subfield sub-code. Compared with all the known capacity-achieving T-PIR schemes for the same nontrivial parameters, ours is the first explicit capacity-achieving T-PIR scheme over the binary field.

Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications

Emotion is one of the main reasons why people engage and interact with music <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . Songs can express our inner feelings, produce goosebumps, bring us to tears, share an emotional state with a composer or performer, or trigger specific memories. Interest in a deeper understanding of the relationship between music and emotion has motivated researchers from various areas of knowledge for decades <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , including computational researchers. Imagine an algorithm capable of predicting the emotions that a listener perceives in a musical piece, or one that dynamically generates music that adapts to the mood of a conversation in a film—a particularly fascinating and provocative idea. These algorithms typify music emotion recognition (MER), a computational task that attempts to automatically recognize either the emotional content in music or the emotions induced by music to the listener <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . To do so, emotionally relevant features are extracted from music. The features are processed, evaluated, and then associated with certain emotions. MER is one of the most challenging high-level music description problems in music information retrieval (MIR), an interdisciplinary research field that focuses on the development of computational systems to help humans better understand music collections. MIR integrates concepts and methodologies from several disciplines, including music theory, music psychology, neuroscience, signal processing, and machine learning.

Detection and Extraction of Electronic Information During Investigative (Search) Actions Under Ukrainian Legislation

The article provides a comprehensive study of the features of detection and seizure of electronic information during investigative (search) actions under the laws of Ukraine. The methodological basis of the scientific article is the complex application of general scientific and special methods of scientific knowledge in their relationship, selected considering the purpose and objectives of the study, its object and subject. It is noted that the following information, which is contained in the form of electronic-digital mappings, can be obtained during investigative (search) actions, covert investigative (search) actions, as well as during temporary access to things and documents as a measure of criminal proceedings. It is established that the process of reviewing electronic documents in general corresponds to the generally accepted algorithm of actions: search and detection of documents; visual inspection of the external state without changing the conditions of perception; fixation; detection of handprints; detection of traces of changes in the original content; preparation for packaging; packaging. It is substantiated that the involvement of a computer technician in the investigation, search, other procedural actions, aimed at solving the problems of contextual information retrieval, detection of electronic document management programs, viewing the history of visits to Internet browsers.

A geometric framework for pitch estimation on acoustic musical signals

This paper presents a geometric approach to pitch estimation (PE) – an important problem in music information retrieval (MIR), and a precursor to a variety of other problems in the field. Though there exist a number of highly accurate methods, both mono-pitch estimation and multi-pitch estimation (particularly with unspecified polyphonic timbre) prove computationally and conceptually challenging. A number of current techniques, while incredibly effective, are not targeted towards eliciting the underlying mathematical structures that underpin the complex musical patterns exhibited by acoustic musical signals. Tackling the approach from both theoretical and experimental perspectives, we present a novel framework, a basis for further work in the area, and results that (while not state of the art) demonstrate relative efficacy. The framework presented in this paper opens up a completely new way to tackle PE problems and may have uses both in traditional analytical approaches as well as in the emerging machine learning (ML) methods that currently dominate the literature.

The Capacity of Private Information Retrieval Under Arbitrary Collusion Patterns for Replicated Databases

We study the private information retrieval (PIR) problem under arbitrary collusion patterns for replicated databases. We find a general characterization of the PIR capacity, which is the same as the capacity of the original PIR problem with the number of databases N replaced by a number S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> . S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> is the optimal solution to a linear programming problem that is a function of the incidence matrix of the collusion pattern. Hence, the essence of any collusion pattern can be distilled into one number S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> . In the proposed achievable scheme, databases are non-uniformly queried according to the optimal solution of a linear programming problem based on the collusion pattern. It can be seen that the databases who collude more with others are queried less. In the converse proof, Shearer's lemma is applied, in place of Han's inequality, to a linear combination of inequalities, where each inequality corresponds to one colluding set in the collusion pattern. The weights of the linear combination come from the optimal solution of another linear programming problem based on the collusion pattern. Finally, by noting the interesting fact that the two seemingly different linear programming problems, one used in the achievability proof and the other used in the converse proof, are in fact dual problems, we characterize the capacity of the PIR problem under arbitrary collusion patterns.

Word-embedding-based query expansion: Incorporating Deep Averaging Networks in Arabic document retrieval

One of the main issues associated with search engines is the query–document vocabulary mismatch problem, a long-standing problem in Information Retrieval (IR). This problem occurs when a user query does not match the content of stored documents, and it affects most search tasks. Automatic query expansion (AQE) is one of the most common approaches used to address this problem. Various AQE techniques have been proposed; these mainly involve finding synonyms or related words for the query terms. Word embedding (WE) is one of the methods that are currently receiving significant attention. Most of the existing AQE techniques focus on expanding the individual query terms rather the entire query during the expansion process, and this can lead to query drift if poor expansion terms are selected. In this article, we introduce Deep Averaging Networks (DANs), an architecture that feeds the average of the WE vectors produced by the Word2Vec toolkit for the terms in a query through several linear neural network layers. This average vector is assumed to represent the meaning of the query as a whole and can be used to find expansion terms that are relevant to the complete query. We explore the potential of DANs for AQE in Arabic document retrieval. We experiment with using DANs for AQE in the classic probabilistic BM25 model as well as for two recent expansion strategies: Embedding-Based Query Expansion approach (EQE1) and Prospect-Guided Query Expansion Strategy (V2Q). Although DANs did not improve all outcomes when used in the BM25 model, it outperformed all baselines when incorporated into the EQE1 and V2Q expansion strategies.

On the Fundamental Limits of Cache-Aided Multiuser Private Information Retrieval

We consider the problem of cache-aided Multiuser Private Information Retrieval (MuPIR) which is an extension of the single-user cache-aided PIR problem to the case of multiple users. In MuPIR, each of the $K_{\rm u}$ cache-equipped users wishes to privately retrieve a message out of $K$ messages from $N$ databases each having access to the entire message library. The privacy constraint requires that any individual database learns nothing about the demands of all users. The users are connected to each database via an error-free shared-link. In this paper, we aim to characterize the optimal trade-off between users' memory and communication load for such systems. Based on the proposed novel approach of \emph{cache-aided interference alignment (CIA)}, first, for the MuPIR problem with $K=2$ messages, $K_{\rm u}=2$ users and $N\ge 2$ databases, we propose achievable retrieval schemes for both uncoded and general cache placement. The CIA approach is optimal when the cache placement is uncoded. For general cache placement, the CIA approach is optimal when $N=2$ and $3$ verified by the computer-aided approach. Second, when $K,K_{\rm u}$ and $N$ are general, we propose a new \emph{product design} (PD) which incorporates the PIR code into the linear caching code. The product design is shown to be order optimal within a multiplicative factor of 8 and is exactly optimal when the user cache memory size is large.

Asymmetric Leaky Private Information Retrieval

Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of K messages from N non-colluding replicated databases without learning anything about the remaining K-1 messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant ϵ. For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant δ. We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary ϵ and δ. We show that the optimal download cost of AL-PIR is upper-bounded as D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (ϵ,δ) ≤ 1+\frac 1N-1-\frac δe <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sup> N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> -1. Second, we obtain an information-theoretic lower bound on the download cost as D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (ϵ,δ) ≥ 1+\frac 1Ne <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sup> -1-\frac δ(Ne <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> -1. The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when ϵ = 0, i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of \frac N-e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-ϵ</sup> N-1 for any ϵ > 0 and δ > 0.

Developing unsupervised knowledge-enhanced models to reduce the semantic gap in information retrieval

In this thesis we tackle the semantic gap, a long-standing problem in Information Retrieval (IR). The semantic gap can be described as the mismatch between users' queries and the way retrieval models answer to such queries. Two main lines of work have emerged over the years to bridge the semantic gap: (i) the use of external knowledge resources to enhance the bag-of-words representations used by lexical models, and (ii) the use of semantic models to perform matching between the latent representations of queries and documents. To deal with this issue, we first perform an in-depth evaluation of lexical and semantic models through different analyses [Marchesin et al., 2019]. The objective of this evaluation is to understand what features lexical and semantic models share, if their signals are complementary, and how they can be combined to effectively address the semantic gap. In particular, the evaluation focuses on (semantic) neural models and their critical aspects. Each analysis brings a different perspective in the understanding of semantic models and their relation with lexical models. The outcomes of this evaluation highlight the differences between lexical and semantic signals, and the need to combine them at the early stages of the IR pipeline to effectively address the semantic gap. Then, we build on the insights of this evaluation to develop lexical and semantic models addressing the semantic gap. Specifically, we develop unsupervised models that integrate knowledge from external resources, and we evaluate them for the medical domain - a domain with a high social value, where the semantic gap is prominent, and the large presence of authoritative knowledge resources allows us to explore effective ways to address it. For lexical models, we investigate how - and to what extent - concepts and relations stored within knowledge resources can be integrated in query representations to improve the effectiveness of lexical models. Thus, we propose and evaluate several knowledge-based query expansion and reduction techniques [Agosti et al., 2018, 2019; Di Nunzio et al., 2019]. These query reformulations are used to increase the probability of retrieving relevant documents by adding to or removing from the original query highly specific terms. The experimental analyses on different test collections for Precision Medicine - a particular use case of Clinical Decision Support (CDS) - show the effectiveness of the proposed query reformulations. In particular, a specific subset of query reformulations allow lexical models to achieve top performing results in all the considered collections. Regarding semantic models, we first analyze the limitations of the knowledge-enhanced neural models presented in the literature. Then, to overcome these limitations, we propose SAFIR [Agosti et al., 2020], an unsupervised knowledge-enhanced neural framework for IR. SAFIR integrates external knowledge in the learning process of neural IR models and it does not require labeled data for training. Thus, the representations learned within this framework are optimized for IR and encode linguistic features that are relevant to address the semantic gap. The evaluation on different test collections for CDS demonstrate the effectiveness of SAFIR when used to perform retrieval over the entire document collection or to retrieve documents for Pseudo Relevance Feedback (PRF) methods - that is, when it is used at the early stages of the IR pipeline. In particular, the quantitative and qualitative analyses highlight the ability of SAFIR to retrieve relevant documents affected by the semantic gap, as well as the effectiveness of combining lexical and semantic models at the early stages of the IR pipeline - where the complementary signals they provide can be used to obtain better answers to semantically hard queries.

Private Information Retrieval Over Gaussian MAC

Consider the problem of Private Information Retrieval (PIR), where a user wishes to retrieve a single message from N non-communicating and non-colluding databases (servers). All servers store the same set of M messages and they respond to the user through a block fading Gaussian Multiple Access Channel (MAC). The goal in this setting is to keep the index of the required message private from the servers while minimizing the communication overhead. This work provides joint privacy and channel coding retrieval schemes for the Gaussian MAC with and without fading. The schemes exploit the linearity of the channel while using the Compute and Forward (CF) coding scheme. Consequently, single-user encoding and decoding are performed to retrieve the required message. In the case of a channel without fading, the achievable retrieval rate is shown to outperform a separation-based scheme, in which the retrieval and the channel coding are designed separately. Moreover, this rate equals the channel capacity without privacy constraints for even N (hence the joint scheme is optimal) and has a negligible gap from the capacity for odd N as N grows. When the channel suffers from fading, the asymmetry between the servers' channels forces a more complicated solution, which involves a hard optimization problem. Nevertheless, we provide coding schemes and lower bounds on the expected achievable retrieval rate which are shown to have the same scaling laws as the channel capacity, both in the number of servers and the SNR.

Improved reviewer assignment based on both word and semantic features

Assigning appropriate reviewers to a manuscript from a pool of candidate reviewers is a common challenge in the academic community. Current word- and semantic-based approaches treat the reviewer assignment problem (RAP) as an information retrieval problem but do not take into account two constraints of the RAP: incompleteness of the reviewer data and interference from nonmanuscript-related papers. In this paper, a word and semantic-based iterative model (WSIM) is proposed to account for the constraints of the RAP by improving the similarity calculations between reviewers and manuscripts. First, we use the improved language model and topic model to extract word features and semantic features to represent reviewers and manuscripts. Second, we use a similarity metric based on the normalized discounted cumulative gain (NDCG) to measure semantic similarity. This metric ignores the probability value (quantitative exact value) of the topic and considers only the ranking (qualitative relevance), thus reducing overfitting to incomplete reviewer data. Finally, we use an iterative model to reduce the interference from nonmanuscript-related papers in the reviewer data. This approach considers the similarity between the manuscript and each of the reviewer’s papers. We evaluate the proposed WSIM on two real datasets and compare its performance to that of seven existing methods. The experimental results show that the WSIM improves the recommendation accuracy by at least 2.5% on the top 20.

Using a problem-based approach to improve the professional readiness of students of physical and mathematical specialties

The use of a problem-based approach in the preparation of students of physical and mathematical specialties allows them to acquire new skills, abilities and knowledge in the process of learning by solving various professional problems. This requires the use of various available information resources.Problem-oriented training provides an opportunity to acquire the skills of information retrieval and creative problem solving. During problem-oriented training, students have the opportunity to acquire the skills of teamwork and search for non-standard ways to solve problematic issues. Professional readiness is an integral indicator of the quality of training of a specialist and the level of skill in accordance with the requirements that his professional activity puts forward. Problem-oriented learning encourages students to search for the required information from various sources. In addition, the information sought can be interdisciplinary. Problem-oriented training contributes to the improvement of professional readiness of students of physical and mathematical specialties.

Low-Energy Acceleration of Binarized Convolutional Neural Networks Using a Spin Hall Effect Based Logic-in-Memory Architecture

Deep Learning (DL) offers the advantages of high accuracy performance at tasks such as image recognition, learning of complex intelligent behaviors, and large-scale information retrieval problems such as intelligent web search. To attain the benefits of DL, the high computational and energy-consumption demands imposed by the underlying processing, interconnect, and memory devices on which software-based DL executes can benefit substantially from innovative hardware implementations. Logic-in-Memory (LIM) architectures offer potential approaches to attaining such throughput goals within area and energy constraints starting with the lowest layers of the hardware stack. In this paper, we develop a Spintronic Logic-in-Memory (S-LIM) XNOR neural network (S-LIM XNN) which can perform binary convolution with reconfigurable in-memory logic without supplementing distinct logic circuits for computation within the memory module itself. Results indicate that the proposed S-LIM XNN designs achieve 1.2-fold energy reduction, 1.26-fold throughput increase, and 1.4-fold accuracy improvement compared to the state-of-the-art binarized convolutional neural network hardware. Design considerations, architectural approaches, and the impact of process variation on the proposed hybrid spin-CMOS design are identified and assessed, including comparisons and recommendations for future directions with respect to LIM approaches for neuromorphic computing.

Private Information Retrieval for a Multi-Message Scenario With Private Side Information

We consider the problem of private information retrieval (PIR), where a single user with private side information (PSI) aims to retrieve multiple files from a library stored at some servers. We assume that the side information (SI) at the user includes a subset of files stored privately. Moreover, the identity of requests and side information at the user are not revealed to any of the servers. The problem involves finding the minimum load transmitted from the servers to the user such that the requested files can be decoded with the help of received data and side information. By providing matching lower and upper bounds for certain regimes, we characterize the minimum load imposed on all the servers. Our result shows that the capacity is the same as the capacity of a multi-message PIR problem without PSI, but with a library of reduced size, i.e., the library is equal to the original library size minus the size of SI. Finally, we extend our setup to the case where instead of storing complete files as SI, the user can store a fraction of files. For this scenario, we propose an achievability scheme based on which we discuss the best storing strategies.