NOWADAYS, THE EXPANDED USE OF LBSS INVOLVES OPPORTUNITIES TO THE ADVERSARIES THREATENING THE LOCATION PRIVACY OF MOBILE USERS. SEVERAL APPROACHES HAVE BEEN PROPOSED TO TACKLE EITHER LOCATION PRIVACY, LOCATION SAFETY, AND QUERY PRIVACY INDEPENDENTLY. IN THIS PAPER, WE PRESENT A WORK IN PROGRESS, WHICH AIMS TO PROPOSE A UNIFIED FRAMEWORK TO PROTECT PRIVACY IN ALL THESE DIMENSIONS SIMULTANEOUSLY. THE DEMAND FOR QUERY-PRIVACY PROTECTION FOR MANY USERS WILL BE ADDRESSED IN BATCH.

BIG DATA IS A GENERIC TERM THAT INVOLVES THE STORING AND PROCESSING OF A LARGE AMOUNT OF DATA. THIS LARGE AMOUNT OF DATA HAS BEEN PROMOTED BY TECHNOLOGIES SUCH AS MOBILE APPLICATIONS, INTERNET OF THINGS (IOT), AND GEOGRAPHIC INFORMATION SYSTEMS (GIS). AN EXAMPLE OF GIS IS A SPATIO-TEMPORAL DATABASE (STDB). A COMPLEX PROBLEM TO ADDRESS IN TERMS OF PROCESSING TIME IS PATTERN SEARCHING ON STDB. NOWADAYS, HIGH INFORMATION PROCESSING CAPACITY IS AVAILABLE EVERYWHERE. NEVERTHELESS, THE PATTERN SEARCHING PROBLEM ON STDB USING TRADITIONAL DATA MINING TECHNIQUES IS COMPLEX BECAUSE THE DATA INCORPORATE THE TEMPORAL ASPECT. TRADITIONAL TECHNIQUES OF PATTERN SEARCHING, SUCH AS TIME SERIES, DO NOT INCORPORATE THE SPATIAL ASPECT. FOR THIS REASON, TRADITIONAL ALGORITHMS BASED ON ASSOCIATION RULES MUST BE ADAPTED TO FIND THESE PATTERNS. MOST OF THE ALGORITHMS TAKE EXPONENTIAL PROCESSING TIMES. IN THIS PAPER, A NEW EFFICIENT ALGORITHM (NAMED MINUS-F1) TO LOOK FOR PERIODIC PATTERNS ON STDB IS PRESENTED. OUR ALGORITHM IS COMPARED WITH APRIORI, MAX-SUBPATTERN, AND PPA ALGORITHMS ON SYNTHETIC AND REAL STDB. ADDITIONALLY, THE COMPUTATIONAL COMPLEXITIES FOR EACH ALGORITHM IN THE WORST CASES ARE PRESENTED. EMPIRICAL RESULTS SHOW THAT MINUS-F1 IS NOT ONLY MORE EFFICIENT THAN APRIORI, MAX-SUBPATTERN, AND PAA, BUT ALSO IT PRESENTS A POLYNOMIAL BEHAVIOR.

THIS PAPER PRESENTS A SUBSCRIPTION-BASED OVERLAY NETWORK THAT SUPPORTS FILE PARALLEL DOWNLOADING FOR CLOUD COLLABORATION. FIRST, OUR SYSTEM LETS USERS TO REGISTER TO A CENTRAL SERVER AND ALLOWS THIS SERVER TO INCREMENTALLY BUILD A TOPOLOGY GRAPH CONTAINING THE NETWORK CONNECTIONS AMONG THE SUBSCRIBERS. WITH THIS TOPOLOGY GRAPH IN PLACE, WE PLAN TO ADDRESS THE CHALLENGES OF MINIMIZING NETWORK TRAFFIC AND CHOOSING THE BEST SET OF NODES STORING A CHOSEN FILE FOR PARALLEL DOWNLOADING. WHEN A SUBSCRIBER WANTS TO ACCESS A CHOSEN FILE STORED IN THE CLOUD, OUR SYSTEM OBTAINS FOR HER A LIST OF NODES HAVING THIS FILE. NODES IN THIS LIST, ARE SORTED CONSIDERING BOTH THEIR NETWORK DISTANCE TO THE SUBSCRIBER AND THEIR WORKLOADS. SECOND, SELECTING THOSE TOP NODES, A BANDWIDTH-AWARE PARALLEL DOWNLOADING TECHNIQUE IS EXECUTED. FINALLY, OUR PROPOSED SYSTEM ALSO FEATURES LEVERAGING IDLING NODES FOR FILE DOWNLOADING. MORE SPECIFICALLY, THE SUBSCRIBERS WHO ARE ON-LINE BUT NOT PARTICIPATING IN DOWNLOADING ARE RECRUITED TO REDUCE BOTH NETWORK TRAFFIC AND AVERAGE LATENCY.

DERIVING INSIGHT FROM DATA IS A CHALLENGING TASK FOR RESEARCHERS AND PRACTITIONERS, ESPECIALLY WHEN WORKING ON SPATIO-TEMPORAL DOMAINS. IF PATTERN SEARCHING IS INVOLVED, THE COMPLICATIONS INTRODUCED BY TEMPORAL DATA DIMENSIONS CREATE ADDITIONAL OBSTACLES, AS TRADITIONAL DATA MINING TECHNIQUES ARE INSUFFICIENT TO ADDRESS SPATIO-TEMPORAL DATABASES (STDBS). WE HEREBY PRESENT A NEW ALGORITHM, WHICH WE REFER TO AS F1/FP, AND CAN BE DESCRIBED AS A PROBABILISTIC VERSION OF THE MINUS-F1 ALGORITHM TO LOOK FOR PERIODIC PATTERNS. TO THE BEST OF OUR KNOWLEDGE, NO PREVIOUS WORK HAS COMPARED THE MOST CITED ALGORITHMS IN THE LITERATURE TO LOOK FOR PERIODIC PATTERNS?NAMELY, APRIORI, MS-APRIORI, FP-GROWTH, MAX-SUBPATTERN, AND PPA. THUS, WE HAVE CARRIED OUT SUCH COMPARISONS AND THEN EVALUATED OUR ALGORITHM EMPIRICALLY USING TWO DATASETS, SHOWCASING ITS ABILITY TO HANDLE DIFFERENT TYPES OF PERIODICITY AND DATA DISTRIBUTIONS. BY CONDUCTING SUCH A COMPREHENSIVE COMPARATIVE ANALYSIS, WE HAVE DEMONSTRATED THAT OUR NEWLY PROPOSED ALGORITHM HAS A SMALLER COMPLEXITY THAN THE EXISTING ALTERNATIVES AND SPEEDS UP THE PERFORMANCE REGARDLESS OF THE SIZE OF THE DATASET. WE EXPECT OUR WORK TO CONTRIBUTE GREATLY TO THE MINING OF ASTRONOMICAL DATA AND THE PERMANENTLY GROWING ONLINE STREAMS DERIVED FROM SOCIAL MEDIA.

WEB SEARCH USERS ARE LIKELY TO FACE PROBLEMS RELATED TO THE AVAILABILITY OF LARGE AMOUNTS OF DATA. AS THE QUANTITY OF ONLINE CONTENT GROWS, THE RISK OF MISSING RELEVANT INFORMATION DURING SEARCH CAN ONLY INCREASE. MOREOVER, EXTERNAL VARIABLES SUCH AS THE USERS? READING PROFICIENCY LEVEL CAN FURTHER COMPLICATE THE TASK. THIS ARTICLE PROPOSES AN ONLINE MULTI-DOCUMENT SUMMARIZATION ALGORITHM FOR TEXT READABILITY, AS A MEANS TO SIMPLIFY WEB SEARCH. THE ALGORITHM IS DESIGNED TO WORK OVER COLLECTIONS OF TOPIC-RELATED DOCUMENTS, SUCH AS THE ONES RETURNED AS THE RESULTS TO A WEB QUERY. CONTRARY TO MOST MODERN APPROACHES, NO PRELIMINARY TRAINING FOR THE ALGORITHM IS REQUIRED. THE ALGORITHM WAS TESTED IN BOTH ENGLISH AND SPANISH LANGUAGE DOCUMENTS, USING DIFFERENT METRICS OF TERM AND SENTENCE RELEVANCE. THE RESULTS WERE COMPARED AGAINST SUMMARIES CREATED BY BOTH HUMAN SUMMARIZERS AND THIRD-PARTY AUTOMATIC TEXT SUMMARIZATION (ATS) SYSTEMS IN TERMS OF TWO VARIABLES: READABILITY AND INFORMATION CONTENT. IN BOTH VARIABLES, THE RESULTS SHOW GENERALIZED GAINS WITH RESPECT TO BOTH THE HUMAN SUMMARIZERS AND THE THIRD-PARTY ATS SYSTEMS. FURTHERMORE, THE ALGORITHM ACHIEVED THESE RESULTS WITH A TIME COMPLEXITY STRICTLY LOWER THAN ; WELL BELOW TRADITIONAL MACHINE LEARNING APPROACHES.

LOCATION-BASED SERVICES (LBSS) HAVE BECOME A PROFITABLE MARKET BECAUSE THEY OFFER REAL-TIME AND LOCAL INFORMATION TO THEIR USERS. ALTHOUGH SEVERAL BENEFITS ARE OBTAINED FROM THE USAGE OF LBSS, THEY HAVE OPENED UP MANY PRIVACY AND SAFETY CHALLENGES BECAUSE A USER NEEDS TO RELEASE HIS/HER LOCATION. TO TACKLE THESE CHALLENGES, MANY LOCATION-CLOAKING TECHNIQUES HAVE BEEN PROPOSED. EVEN THOUGH THESE SOLUTIONS ARE EFFECTIVE IN PROTECTING EITHER LOCATION PRIVACY OR LOCATION SAFETY, THEY DO NOT PROVIDE UNIFIED PROTECTION. FURTHERMORE, MOST OF THEM DO NOT ADDRESS THE POTENTIAL BOTTLENECK IN THE ANONYMITY SERVER AS A HIGH DEMAND OF LOCATION AND SAFETY PROTECTION IS REQUESTED. FINALLY, THEY DO NOT TAKE INTO ACCOUNT THE POTENTIAL IMPACT OF PROCESSING A LARGE AMOUNT OF LOCATION-CLOAKED QUERIES. THIS PAPER DEALS WITH THE EFFICIENT CONSTRUCTION OF LOCATION-CLOAKING AREAS FOR MANY USERS, WHO HAVE BOTH PRIVACY AND SAFETY REQUIREMENTS. TO ACHIEVE THIS GOAL, THE CONSTRUCTION OF LOCATION-CLOAKING AREAS IS CARRIED OUT IN BATCHES. THE LBSS? BATCH PROCESSING TAKES ADVANTAGE OF USERS WHO ARE CLOSE TO EACH OTHER AND WHO HAVE SIMILAR REQUIREMENTS. TWO BATCHING TECHNIQUES TO BUILD CLOAKING REGIONS ARE ANALYZED USING SIMULATIONS. EMPIRICAL RESULTS SHOW OUR TECHNIQUES ARE ABLE TO BALANCE THE ANONYMIZER WORKLOAD, QUALITY OF LOCATION PRIVACY AND SAFETY PROTECTION, AND LBS WORKLOAD.

SOCIAL NETWORKS SUCH AS TWITTER PROVIDE THOUSANDS OF TERABYTES PER DAY, WHICH CAN BE EXPLOITED TO FIND RELEVANT INFORMATION. THIS RELEVANT INFORMATION IS USED TO PROMOTE MARKETING STRATEGIES, ANALYZE CURRENT POLITICAL ISSUES, AND TRACK MARKET TRENDS, TO NAME A FEW EXAMPLES. ONE INSTANCE OF RELEVANT INFORMATION IS FINDING CYCLIC BEHAVIOR PATTERNS (I.E., PATTERNS THAT FREQUENTLY REPEAT THEMSELVES OVER TIME) IN THE POPULATION. BECAUSE TRENDING TOPICS ON TWITTER CHANGE RAPIDLY, EFFICIENT ALGORITHMS ARE REQUIRED, ESPECIALLY WHEN CONSIDERING LOCATION AND TIME (I.E., THE SPECIFIC LOCATION AND TIME) DURING BROADCASTS. THIS ARTICLE PRESENTS AN EFFICIENT ALGORITHM BASED ON ASSOCIATION RULES TO FIND CYCLICAL PATTERNS ON TWITTER, CONSIDERING THE INHERENT SPATIO-TEMPORAL ATTRIBUTES OF DATA. USING A HASH TABLE ENHANCES THE EFFICIENCY OF THIS ALGORITHM, CALLED HASHCYCLE. NOTABLY, HASHCYCLE DOES NOT USE MINIMUM SUPPORT AND CAN DETECT PATTERNS IN A SINGLE RUN OVER A SEQUENCE. THE PROCESSING TIMES OF HASHCYCLE WERE COMPARED TO THE APRIORI (WHICH IS A WELL-KNOWN AND WIDELY USED ON DIVERSE PLATFORMS) AND PROJECTION-BASED PARTIAL PERIODIC PATTERNS (PPA) ALGORITHMS (WHICH IS ONE OF THE MOST EFFICIENT ALGORITHMS IN TERMS OF PROCESSING TIMES). EMPIRICAL RESULTS FROM TWO SPATIO-TEMPORAL DATABASES (A SYNTHETIC DATA SET AND ONE BASED ON TWITTER) SHOW THAT HASHCYCLE HAS MORE EFFICIENT PROCESSING TIMES THAN TWO STATE-OF-THE-ART ALGORITHMS: APRIORI AND PPA.

IT IS WELL DOCUMENTED THAT THE AVERAGE LENGTH OF THE QUERIES SUBMITTED TO WEB SEARCH ENGINES IS RATHER SHORT, WHICH NEGATIVELY IMPACTS THE ENGINES? PERFORMANCE, AS MEASURED BY THE PRECISION METRIC. IT IS ALSO WELL KNOWN THAT AMBIGUOUS KEYWORDS IN A QUERY MAKE IT HARD TO IDENTIFY WHAT EXACTLY SEARCH ENGINE USERS ARE LOOKING FOR. ONE WAY TO TACKLE THIS CHALLENGE IS TO CONSIDER THE CONTEXT IN WHICH THE QUERY IS SUBMITTED, MAKING USE OF QUERY-SENSITIVE SIMILARITY MEASURES (QSSM). IN THIS PAPER, A PARTICULAR QSSM KNOWN AS THE QUERY-DOCUMENT SIMILARITY MEASURE (QDSM) IS EVALUATED, QDSM IS DESIGNED TO DETERMINE THE SIMILARITY BETWEEN TWO QUERIES BASED ON THEIR TERMS AND THEIR RANKED LISTS OF RELEVANT DOCUMENTS. TO THIS EXTENT, F-MEASURE AND THE NEAREST NEIGHBOR (NN) HAVE BEEN EMPLOYED TO ASSESS THIS APPROACH OVER A COLLECTION OF AOL QUERY LOGS. FINAL RESULTS REVEAL THAT BOTH THE AVERAGE LINK ALGORITHM AND WARD?S METHOD PRESENT BETTER RESULTS USING QDSM THAN COSINE SIMILARITY.

IN THIS PAPER WE PRESENT TWO CONTRIBUTIONS: A METHOD TO CONSTRUCT SIMULATED DOCUMENT COLLECTIONS SUITABLE FOR INFORMATION RETRIEVAL EVALUATION AS WELL AS AN APPROACH OF INFORMATION RETRIEVAL USING PAST QUERIES AND BASED ON RESULT COMBINATION. EXPONENTIAL AND ZIPF DISTRIBUTION AS WELL AS BRADFORD?S LAW ARE APPLIED TO CONSTRUCT SIMULATED DOCUMENT COLLECTIONS SUITABLE FOR INFORMATION RETRIEVAL EVALUATION. EXPERIMENTS COMPARING A TRADITIONAL RETRIEVAL APPROACH WITH OUR APPROACH BASED ON PAST QUERIES USING PAST QUERIES SHOW ENCOURAGING IMPROVEMENTS USING OUR APPROACH.

MUCH OF THE RESEARCH IN INFORMATION RETRIEVAL (IR) IS DEVOTED TO STUDYING THE IMPROVEMENT OF PERSONALIZED RESULTS FOR SPECIFIC USERS IN A STATIC ENVIRONMENT. NEVERTHELESS, FEW APPROACHES TAKE ADVANTAGE OF COLLECTIVE PAST SEARCHES IN A DYNAMIC CONTEXT WHERE THE NUMBER OF DOCUMENTS IS INCREASED ACCORDING WITH THE PASSAGE OF TIME. IN THIS PAPER, WE PRESENT AN ON-LINE PROBABILISTIC ALGORITHM, WHICH USES THE COLLECTIVE PAST SEARCHES IN A DYNAMIC CONTEXT TO ANSWER STATIC AND DYNAMIC QUERIES. SEVERAL EXPERIMENTS WERE CARRIED OUT WITH THE AIM OF EVALUATING THE EFFECTIVENESS OF OUR ALGORITHM. THE ALGORITHM S RESULTS WERE COMPARED WITH THE COSINE MEASURE. FOLLOWING THE CRANFIELD PARADIGM, SIMULATED DATASETS WERE USED IN THE EXPERIMENTS. FINAL RESULTS SHOW THAT IT IS POSSIBLE TO IMPROVE EFFECTIVENESS IN A DYNAMIC CONTEXT.

WEB SEARCH ENGINES PROCESS SEVERAL MILLIONS OF QUERIES PER SECOND OVER SEVERAL BILLIONS OF DOCUMENTS. WITHOUT ANY OPTIMIZATION, THIS PROCESS CAN BE VERY EXPENSIVE IN TERMS OF PROCESSING TIMES. IN THIS REGARD, APPROPRIATE USE OF COMPUTING POWER IS ESSENTIAL. ONE WAY TO TACKLE THIS PROBLEM IS THROUGH THE USE OF CACHING MECHANISMS. KEEP IN MIND, MOST RESEARCH BASED ON CACHING MECHANISMS USES REPETITIVE QUERIES-IT MEANS QUERIES SYNTACTICALLY EQUALS-TO CONFORM CACHES. FURTHERMORE, THE UNIVERSE OF REPETITIVE QUERIES IS SMALL IN COMPARISON WITH A SET OF SIMILAR SEMANTICALLY QUERIES. THIS PAPER PRESENTS A DYNAMIC CACHE THAT RELIES ON AN ONLINE ALGORITHM, WHICH PERFORMS A SEMANTIC MATCH BETWEEN THE USER?S QUERY AND QUERIES STORED IN THE CACHE. BROADLY SPEAKING, THE ALGORITHM EMPLOYS A PRIORITY QUEUE, WHERE POPULAR QUERIES ARE STORED ALONG WITH THEIR RELEVANT DOCUMENTS. EMPIRICAL RESULTS SHOW THAT OUR PROPOSED APPROACH IMPROVES THE RESPONSE TIMES AND PRECISION. MOREOVER, THE USE OF SEMANTICALLY RELATED KEYWORDS PROVES TO BE A KEY CONTRIBUTION THAT HAD BEEN OVERLOOKED IN PREVIOUS RESEARCH.

NOWADAYS, PEOPLE CAN ACCESS A WIDE RANGE OF APPLICATIONS AND SERVICES FOR MOBILE DEVICE USERS. AMONG THEM, LOCATION-BASED SERVICES (LBS), WHERE THE APPLICATION NEEDS THE USER?S POSITION TO PROVIDE THE SERVICE. SOME EXAMPLES OF THESE APPLICATIONS ARE UBER AND WAZE. NEVERTHELESS, THE REPETITIVE USE OF AN LBS CAN REVEAL CONFIDENTIAL USER INFORMATION; THUS, BEHAVIOR PATTERNS?SUCH AS DAILY ROUTES?COULD BE DEDUCED BY SOME DISHONEST LBS. FURTHERMORE, A QUERY?S KEYWORDS COULD PROVIDE INFORMATION ABOUT A USER?S HEALTH STATUS OR FUTURE POSITION WHEN IT INQUIRES ABOUT HOSPITALS OR HOTELS. THEREFORE, AN ADVERSARY CAN USE THIS INFORMATION FOR UNETHICAL PURPOSES, AND USERS NEED MECHANISMS THAT PROTECT THEIR PRIVACY. AT PRESENT, SEVERAL APPROACHES SEPARATELY TACKLE LOCATION PRIVACY, LOCATION SECURITY, AND QUERY PRIVACY. TO THE BEST OF OUR KNOWLEDGE, NO PREVIOUS WORK DEALS WITH ALL THESE MENTIONED ASPECTS SIMULTANEOUSLY ESPECIALLY WHEN USERS DEMAND CONTINUOUS PROTECTION WHEN MOVING AND ACCESSING AN LBS. THIS PAPER PROPOSES TWO BATCH TECHNIQUES TO PROVIDE LOCATION PRIVACY, LOCATION SAFETY, AND QUERY PRIVACY IN AN ENVIRONMENT THAT CONSIDERS A CONTINUOUS LBS. THESE TECHNIQUES APPLY -DIVERSITY (QUERY PRIVACY) IN A CONTEXT THAT CONTEMPLATES QUERY SEMANTICS, AS WELL AS A DIVERSE SET OF USERS? PATHS. EXTENSIVE EXPERIMENTATION SHOWS THAT BOTH TECHNIQUES ARE COST-EFFECTIVE AND SCALABLE SOLUTIONS THAT OFFER UNIFIED LOCATION PRIVACY, QUERY PRIVACY, AND LOCATION SAFETY PROTECTION FOR MANY MOBILE USERS.

WHEN USING INFORMATION RETRIEVAL SYSTEMS, INFORMATION RELATED TO SEARCHES IS TYPICALLY STORED IN FILES, WHICH ARE WELL KNOWN AS LOG FILES. BY CONTRAST, PAST SEARCH RESULTS OF PREVIOUSLY SUBMITTED QUERIES ARE IGNORED MOST OF THE TIME. NEVERTHELESS, PAST SEARCH RESULTS CAN BE PROFITABLE FOR NEW SEARCHES. SOME APPROACHES IN INFORMATION RETRIEVAL EXPLOIT THE PREVIOUS SEARCHES IN A CUSTOMIZABLE WAY FOR A SINGLE USER. ON THE CONTRARY, APPROACHES THAT DEAL WITH PAST SEARCHES COLLECTIVELY ARE LESS COMMON. THIS PAPER DEALS WITH SUCH AN APPROACH, BY USING PAST RESULTS OF SIMILAR PAST QUERIES SUBMITTED BY OTHER USERS, TO BUILD THE ANSWERS FOR NEW SUBMITTED QUERIES. IT PROPOSES TWO MONTE CARLO ALGORITHMS TO BUILD THE RESULT FOR A NEW QUERY BY SELECTING RELEVANT DOCUMENTS ASSOCIATED TO THE MOST SIMILAR PAST QUERY. EXPERIMENTS WERE CARRIED OUT TO EVALUATE THE EFFECTIVENESS OF THE PROPOSED ALGORITHMS USING SEVERAL DATASET VARIANTS. THESE ALGORITHMS WERE ALSO COMPARED WITH THE BASELINE APPROACH BASED ON THE COSINE MEASURE, FROM WHICH THEY REUSE PAST RESULTS. SIMULATED DATASETS WERE DESIGNED FOR THE EXPERIMENTS, FOLLOWING THE CRANFIELD PARADIGM, WELL ESTABLISHED IN THE INFORMATION RETRIEVAL DOMAIN. THE EMPIRICAL RESULTS SHOW THE INTEREST OF OUR APPROACH.

WE PLAN TO ADDRESS THE PROBLEM OF PROCESSING LOCATION-BASED QUERIES (LBQ) IN A MANET FOR PRESERVING QUERY PRIVACY AS MUCH AS POSSIBLE. OUR IDEA IS THAT MOBILE USERS WILL FIRST ASK THEMSELVES FOR SOLVING A QUERY BEFORE ANY USER DECIDES TO SUBMIT ITS QUERY TO AN UNTRUSTED LBS. OUR FIRST GOAL IS TO DEFINE A COLLABORATIVE CACHING STRATEGY TO BE RUN BY THE MOBILE USERS THEMSELVES THAT EXPLOIT THE GEOGRAPHIC AND SEMANTIC SIMILARITIES AMONG THE LBQS TO PERFORM EFFICIENT PROCESSING OF LCQS. OUR SECOND GOAL TO PROTECT A USER?S QUERY PRIVACY WHEN ANY USER SUBMITS AN LBQ TO THE LBS; WE ARE PLANNING TO DEVELOP A DISTRIBUTED ALGORITHM TO PROVIDE L-DIVERSITY ONLY WHEN THIS PROTECTION IS USEFUL. EXISTING CACHING TECHNIQUES FOR MANET DO NOT EXPLOIT SEMANTIC AND GEOGRAPHIC SIMILARITIES AMONG THE LBQS, AND THEY ASSUME MOBILE USERS HAVE ACCESS TO SOME STORAGE INFRASTRUCTURE TO MAINTAIN GLOBAL INFORMATION TO COMPUTE L-DIVERSITY.